model risk presentation

The potential loss an institution may incur as a consequence of decisions that are principally based on the output of internal models

What is Model Risk?

Model risk is the potential loss an institution may incur as a consequence of decisions that are principally based on the output of internal models as a result of errors in the development, implementation, or use of models.

Understanding Model Risk

Model risk’s become prominent and of serious concern following the advent of the use of sophisticated quantitative models in dealing with applications in major disciplines. The risk arises mainly because of potential errors in the models and appropriate usage and implementation of the model. The errors and inaccuracies can cause considerable monetary losses, poor organizational decision-making, and damage to institutional reputation .

Model risk arises principally because of two reasons:

• The model might have fundamental inaccuracies that produce erroneous results for its intended use.
• The incorrect or inappropriate use of the model.

What is a Model?

Since model risk is caused by the use of models, it’s appropriate to also define a model. A model is a quantitative system or mathematical representation that processes input data to derive quantitative estimates of different variables.

A model contains a set of variable assumptions and data for inputs, processes, outputs, and scenarios. It applies mathematical, statistical, financial, and economic data and techniques in a model. A model contains three major components:

1. Inputs : Data and assumptions of the model

2. Process : Processes that transform inputs into quantitative estimates

3. Reporting : Expression of estimates into valuable information for management

Sources of Model Risk

The following are some of the sources of model risk:

Data used in a model may be inaccurate, incomplete, or distorted. It is crucial in developing an effective model; hence, flawed data has the potential to compromise the whole model.

2. Model implementation

The incorrect and/or incomplete implementation of a model can lead to inaccurate or erroneous results that can have adverse effects on model results and the organizational decision-making process.

3. Methodology

Statistical methodologies have their own errors, such as sampling errors and standard errors, that occur in regression modeling .

4. Parameters and assumption

Unrealistic and incorrect assumptions may alter the intended parameters of a model, thereby inducing risk. When fitting model parameters, an error may result in the calibration of the model.

Inappropriate use of a model may invalidate an otherwise effective model.

6. Interpretation

Misinterpreting the results of a model brings on significant risk as a misinformed course of action is likely to be followed.

7. Inventory

Incomplete and inaccurate model inventory leads to model risk.

Model Risk Management (MRM) Framework

A good model risk management (MRM) framework should be crafted based on industry best practices and conform to regulatory guidelines. An authority to benchmark the MRM framework is the Supervisory Guidance on Model Risk Management (SR 11-07) from the U.S. Federal Reserve.

The MRM framework should include the following processes in its life cycle:

1. Modeling Risk Standards

Minimum standards should be set on the development of a model, and these standards need to be followed and respected. The internal standards should be at the same level or higher compared to regulatory standards such as Supervisory Guidance on Model Risk Management (SR 11-07).

The standards should encompass standards for data quality, model changes, model use, expert judgment, model methodology, model validation, documentation, external model data, and model reporting, among others.

2. Model Risk Appetite

After the establishment of a risk policy, it is prudent that a statement of the Board model risk appetite is well articulated for effective model risk management. Risk appetite is the amount of risk that an organization is prepared and capable of assuming in order to meet its desired objectives.

The level of risk appetite for model risk will depend on the purpose in which the model is applied. Model risk appetite should be stated in terms of risk tolerance and various relevant metrics such as aggregate quantitative risk exposure, the number of high risk-rated models, etc.

3. Model Risk Identification

It is necessary to identify the specific risks that affect the organization. An inventory of existing models should be completed to identify key model changes. The model inventory should categorize features such as the following (among others):

• Description of the purpose of the model
• How the model is used
• Frequency of its use
• Model assumptions or inputs

4. Model Risk Assessment and Measurement

A quantitative and qualitative risk assessment needs to be carried out to assess the model risk of each model. The two approaches will derive an enterprise-wide risk assessment framework.

The quantification of model risk uses various model risk measurement approaches, or they can use operational risk style model approaches.  There are three main techniques to quantify risk notably:

• Sensitivity analysis – Changes in model assumptions and parameters and monitoring of changing outcomes
• Backtesting – Testing a model by using historical data and comparing the output to past results
• Challenger model – Comparing the results of a model with results from another alternative model using the same data

A quantitative assessment will measure and collate each distinct quantifiable model risk assessment through the use of appropriate correlation factors.

Qualitative risk assessment involves consideration for the model fit for purpose. The result will indicate model robustness, which will have an impact on the model risk rating. A qualitative assessment considers the use of qualitative metrics to measure risk in a model – notably model compliance with standards, cumulative model errors, the degree of model risk assessment, and other qualitative factors.

5. Model Risk Mitigation

Possible risk mitigation strategies may include the following:

• Changes in the model’s development process
• Carrying out supplementary model validation considering changes in the nature and structure of existing risks and the emergence of new risks that the organization is exposed to.
• Employment of independent expert judgments on model result interpretation as a result of model uncertainties.
• Model adherence and applicability to new risk regulations
• Model efficiency and applicability enhancement measures to reduce risk such as additional capital can help mitigate risk

6. Model Risk Monitoring and Reporting

The model risk monitoring and reporting function seeks to identify the following issues:

• Monitoring if model risk policy and risk appetite are being adhered to as per policy. The process will recommend if management intervention is required if there is a divergence.
• A material model inventory should be carried out on each individual model to measure if it is being used as per the MRM policy framework.
• Results of model risk assessment and validation should be analyzed, and corrective action should be taken on any weaknesses identified.
• An overview of new trends in model risk management and any other relevant matters.

In summary, an MRM framework should encompass the following:

• Minimum model risk management standards as per regulatory guidelines
• A clear statement of the Board’s model risk appetite
• A risk identification process to reveal model risks that the organization is exposed to and which ones require comprehensive management
• Quantitative and qualitative model risk assessment
• A comprehensive array of model risk mitigation strategies
• A model risk monitoring and reporting framework

The overall risk management framework is as good as its implementation and the people who use it. Hence, an organization should cultivate a good risk culture within the organization.

Additional Resources

CFI offers the Commercial Banking & Credit Analyst (CBCA)®  certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following resources will be helpful:

• Credit Risk
• Basel Accord
• Risk Shifting
• Financial Modeling Software
• See all financial modeling resources

Analyst Certification FMVA® Program

Below is a break down of subject weightings in the FMVA® financial analyst program. As you can see there is a heavy focus on financial modeling, finance, Excel, business valuation, budgeting/forecasting, PowerPoint presentations, accounting and business strategy.

A well rounded financial analyst possesses all of the above skills!

Additional Questions & Answers

CFI is the global institution behind the financial modeling and valuation analyst  FMVA® Designation . CFI is on a mission to enable anyone to be a great financial analyst and have a great career path. In order to help you advance your career, CFI has compiled many resources to assist you along the path.

In order to become a great financial analyst, here are some more  questions and answers  for you to discover:

• What is Financial Modeling?
• How Do You Build a DCF Model?
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• How Do You Value a Business?
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• Fundamental Analysis

Model Risk: Definition, Management, and Examples

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT).

What Is Model Risk?

Model risk is a type of risk that occurs when a financial model is used to measure quantitative information such as a firm's market risks or value transactions, and the model fails or performs inadequately and leads to adverse outcomes for the firm.

A model is a system, quantitative method, or approach that relies on assumptions and economic, statistical, mathematical, or financial theories and techniques. The model processes data inputs into a quantitative-estimate type of output.

Financial institutions and investors use models to identify the theoretical value of stock prices and to pinpoint trading opportunities. While models can be useful tools in investment analysis , they can also be prone to various risks that can occur from the usage of inaccurate data, programming errors, technical errors, and misinterpretation of the model's outputs.

Key Takeaways

• In finance, models are used extensively to identify potential future stock values, pinpoint trading opportunities, and help company managers make business decisions.
• Model risk is present whenever an insufficiently accurate model is used to make decisions.
• Model risk can stem from using a model with bad specifications, programming or technical errors, or data or calibration errors.
• Model risk can be reduced with model management such as testing, governance policies, and independent review.

Understanding Model Risk

Model risk is considered a subset of operational risk , as model risk mostly affects the firm that creates and uses the model. Traders or other investors who use a given model may not completely understand its assumptions and limitations, which limits the usefulness and application of the model itself.

In financial companies, model risk can affect the outcome of financial securities valuations , but it's also a factor in other industries. A model can incorrectly predict the probability of an airline passenger being a terrorist or the probability or a fraudulent credit card transaction. This can be due to incorrect assumptions, programming or technical errors, and other factors that increase the risk of a poor outcome.

What Does the Concept of Model Risk Tell You?

Any model is a simplified version of reality, and with any simplification, there is the risk that something will fail to be accounted for. Assumptions made to develop a model and inputs into the model can vary widely. The use of financial models has become very prevalent in the past decades, in step with advances in computing power, software applications, and new types of financial securities. Before developing a financial model, companies will often conduct a financial forecast , which is the process by which it determines the expectations of future results.

Some companies, such as banks, employ a model risk officer to establish a financial model risk management program aimed at reducing the likelihood of the bank suffering financial losses due to model risk issues. Components of the program include establishing model governance and policies. It also involves assigning roles and responsibilities to individuals who will develop, test, implement, and manage the financial models on an ongoing basis.

Real World Examples of Model Risk

Long-term capital management.

The Long-Term Capital Management (LTCM) debacle in 1998 was attributed to model risk. In this case, a small error in the firm's computer models was made larger by several orders of magnitude because of the highly leveraged trading strategy LTCM employed.  

At its height, the hedge fund managed over $100 billion in assets and reported annual returns of over 40%. LTCM famously had two Nobel Prize winners in economics as principal shareholders, but the firm imploded due to its financial model that failed in that particular market environment.

JPMorgan Chase

Almost 15 years later, JPMorgan Chase (JPM) suffered massive trading losses from a value at risk (VaR) model that contained formula and operational errors. Risk managers use VaR models to estimate the future losses a portfolio could potentially incur. In 2012, CEO Jamie Dimon's proclaimed "tempest in a teapot" turned out to be a $6.2 billion loss resulting from trades gone wrong in its synthetic credit portfolio (SCP).  

A trader had established large derivative positions that were flagged by the VaR model that existed at the time. In response, the bank's chief investment officer made adjustments to the VaR model, but due to a spreadsheet error in the model, trading losses were allowed to pile up without warning signals from the model.

This was not the first time that VaR models have failed. In 2007 and 2008, VaR models were criticized for failing to predict the extensive losses many banks suffered during the global financial crisis .  

Roger Lowenstein. "When Genius Failed: The Rise and Fall of Long-Term Capital Management." Random House Trade Paperbacks, 2000.

Government Publishing Office. " JPMorgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses ," Page 8. Accessed Sept. 7, 2020.

Government Publishing Office. " The Risks of Financial Modeling: VAR and the Economic Meltdown ," Page 3. Accessed Sept. 7, 2020.

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The evolution of model risk management

The number of models is rising dramatically—10 to 25 percent annually at large institutions—as banks utilize models for an ever-widening scope of decision making. More complex models are being created with advanced-analytics techniques, such as machine learning, to achieve higher performance standards. A typical large bank can now expect the number of models included within its model risk management (MRM) framework to continue to increase substantially.

Among the model types that are proliferating are those designed to meet regulatory requirements, such as capital provisioning and stress testing . But importantly, many of the new models are designed to achieve business needs, including pricing, strategic planning, and asset-liquidity management. Big data and advanced analytics  are opening new areas for more sophisticated models—such as customer relationship management or anti-money laundering and fraud detection.

Insights from benchmarking and MRM best practices

Model risk management (MRM) was addressed as a top-of-mind concern by leading global banks in recent surveys and roundtables conducted in Europe and the United States by McKinsey and Risk Dynamics. The overall number of models varied widely, ranging from 100 to 3,000 per bank; the number of full-time equivalents (FTEs) dedicated to MRM and validation is also highly variable, with European banks dedicating an average of 8 FTEs per €100 billion of assets, while for US banks this average is 19. MRM groups have grown considerably in recent years, and that growth is expected to continue. Most banks said they still rely heavily on the support of external consultants for validation. The time period for validation varies, depending on model intensity. For European banks, model validation can take anywhere from a few days to 30 weeks, whereas in the United States, we found that variation takes between one and 17 weeks. For both US and EU banks, pass/fail rates vary widely by model. The scope of MRM activities varies widely as well, especially for ongoing model monitoring and model implementation. With respect to governance, most of the MRM groups report directly to the chief risk officer (CRO), or to his or her direct report; the boards of these banks typically discuss MRM in at least six meetings per bank.

In probing the model risk management terrain more closely, our research identified important trends and defined a model life cycle, from planning and development through model use, risk appetite, and policies. 1 1. The research was performed by McKinsey Risk Dynamics, which specializes in model risk and validation. Our research also revealed the key questions on the agenda of chief risk officers (exhibit), and the extent to which these questions are being addressed in some of the most important areas.

Model planning and development

Model planning should be well coordinated across the whole bank. While taking great care to maintain the independence of validation, the model-development group should work closely with validation, an approach that controls costs by reducing the number of iterations and overall development time.

Banks are increasingly centralizing model planning and development, with best-practice institutions setting up “centers of excellence”—advanced-analytics centers acting as service providers to business units. They have created three location models: a local model with the bulk of the work close to model owners, each of them with dedicated teams; a hybrid model; and a centralized model, with the bulk of the work performed in the dedicated corporate center.

As talent demands rise, the highly specialized skills needed to develop and validate models are becoming increasingly scarce. Nearly three-quarters of banks said they are understaffed in MRM, so the importance of adjusting the model risk function to favor talent acquisition and retention has become pronounced. Banks are now developing talent solutions combining flexible and scalable resourcing with an outsourcing component.

Best-practice institutions are classifying models (model “tiering”) using a combination of quantitative and qualitative criteria, including materiality and risk exposure (potential financial loss), and regulatory impact. Models are typically prioritized for validation based on complexity and risk associated with model failure or misuse. Model risk is defined according to potential impact (materiality), uncertainty of model parameters, and what the model is used for. The level of validation is located along a continuum, with high-risk models prioritized for full validation and models of low risk assigned light validation. In the majority of banks we surveyed, validation is highly centralized and situated in the risk organization. Outsourcing is increasing at both European and US institutions, as a result of talent constraints.

Most US banks have strengthened the independence of validation, with the head reporting directly to the CRO. In the United States, material models have to be validated in great detail, with systematic replication and the use of challenger models. This approach is not uniformly applied in Europe, where “conceptual” validations are still accepted in many cases. Likewise, model implementation (in operational and production systems) is not validated consistently across EU banks.

Control and monitoring

In the United States, the Federal Reserve is strict about proper deployment of the three lines of defense, with all stakeholders playing their roles: model developers need to continuously monitor their models; validation must make periodic reviews and audits, relying on the right level of rigor and skills. In Europe, implementation of the three lines remains less defined. The regulatory focus is mainly on regulatory models, as opposed to the US approach, where proper control is expected for all material models, whatever their type. Consequently, in the European Union, few banks have a control and governance unit in charge of MRM policies and appetite; in the United States, nearly all banks have an MRM unit.

Model use, risk appetite, and policies

In accordance with best practices, approximately half the surveyed banks have integrated model risk within their risk-appetite statement, either as a separate element or within nonfinancial risks. Only around 20 percent, however, use specific key performance indicators for model risk, mainly based on model performance and open validation findings on models.

All banks have a model governance framework in place, but 60 percent of the group uses it for the main models only (such as internal ratings based or stress testing). Half of the survey group has a model risk policy. For 60 percent of the group, model ownership is held by users, representing the preferred option for institutions that are more advanced in model management, allowing a better engagement of business on data and modeling assumptions. Risk committees authorize model-use exceptions in around 70 percent of cases.

The promise and wider application of models have brought into focus the need for an efficient MRM function, to ensure the development and validation of high-quality models across the whole organization—eventually beyond risk itself. Financial institutions have already invested millions in developing and deploying sophisticated MRM frameworks. In analyzing these investments, we have discovered the ways that MRM is evolving and the best practices for building a systematically value-based MRM function (see sidebar, “Insights from benchmarking and MRM best practices”). This article summarizes our findings.

Model risk and regulatory scrutiny

The stakes in managing model risk have never been higher. When things go wrong, consequences can be severe. With digitization and automation, more models are being integrated into business processes, exposing institutions to greater model risk and consequent operational losses. The risk lies equally in defective models and model misuse. A defective model caused one leading financial institution to suffer losses of several hundred million dollars when a coding error distorted the flow of information from the risk model to the portfolio-optimization process. Incorrect use of models can cause as much (or greater) harm. A global bank misused a risk-hedging tool in a highly aggressive manner and, as a result, passed its value-at-risk limits for nearly a week. The bank eventually detected the risk, but because the risk model it used was inadequately governed and validated, it only adjusted control parameters rather than change its investment strategy. The consequent loss ran into the billions. Another global bank was found in violation of European banking rules and fined hundreds of millions of dollars after it misused a calculation model for counterparty-risk capital requirements.

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Events like these at top institutions have focused financial-industry attention on model risk. Supervisors on both sides of the Atlantic decided that additional controls were needed and began applying specific requirements for model risk management on banks and insurers. In April 2011, the US Board of Governors of the Federal Reserve System published the Supervisory Guidance on Model Risk Management (SR 11-7). This document provided an early definition of model risk that subsequently became standard in the industry: “The use of models invariably presents model risk, which is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports.” SR 11-7 explicitly addresses incorrect model outputs, taking account of all errors at any point from design through implementation. It also requires that decision makers understand the limitations of a model and avoid using it in ways inconsistent with the original intent. The European Banking Authority’s Supervisory Review and Evaluation Process , meanwhile, requires that model risk be identified, mapped, tested, and reviewed. Model risk is assessed as a material risk to capital, and institutions are asked to quantify it accordingly. If the institution is unable to calculate capital needs for a specific risk, then a comprehensible lump-sum buffer must be fixed.

The potential value in mature MRM

The value of sophisticated MRM extends well beyond the satisfaction of regulatory regimes. But how can banks ensure that their MRM frameworks are capturing this value thoroughly? To find the answer, we must first look more closely at the value at stake. Effective MRM can improve an institution’s earnings through cost reduction, loss avoidance, and capital improvement. Cost reduction and loss avoidance come mainly from increased operational and process efficiency in model development and validation, including the elimination of defective models.

Capital improvement comes mainly from the reduction of undue capital buffers and add-ons. When supervisors feel an institution’s MRM is inadequate, they request add-ons. An improved MRM function that puts regulators in a more comfortable position leads to a reduction of these penalties. (The benefit is similar to remediation for noncompliance.) Capital inefficiency is also the result of excessive modeler conservatism. To deal with uncertainty, modelers tend to make conservative assumptions at different points in the models. The assumptions and attending conservatism are often implicit and not well documented or justified. The opacity leads to haphazard application of conservatism across several components of the model and can be costly. Good MRM and proper validation increases model transparency (on model uncertainties and related assumptions) and allows for better judgments from senior management on where and how much conservatism is needed.

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This approach typically leads to the levels of conservatism being presented explicitly, at precise and well-defined locations in models, in the form of overlays subject to management oversight. As a result, the total level of conservatism is usually reduced, as end users better understand model uncertainties and the dynamics of model outcomes. They can then more clearly define the most relevant mitigation strategies, including revisions of policies governing model use.

Profit and loss

With respect to improvement in profit and loss (P&L), MRM reduces rising modeling costs, addressing fragmented model ownership and processes caused by high numbers of complex models. This can save millions. At one global bank, the capital budget for models increased sevenfold in four years, rising from €7 million to €51 million. By gaining a better understanding of the model landscape, banks are able to align model investments with business risks and priorities. By reducing model risk and managing its impact, MRM can also reduce some P&L volatility. The overall effect heightens model transparency and institutional risk culture. The resources released by cost reductions can then be reallocated to high-priority decision-making models.

Systematic cost reduction can only be achieved with an end-to-end approach to MRM. Such an approach seeks to optimize and automate key modeling processes, which can reduce model-related costs by 20 to 30 percent. To take one example, banks are increasingly seeking to manage the model-validation budget, which has been rising because of larger model inventories, increasing quality and consistency requirements, and higher talent costs. A pathway has been found in the industrialization of validation processes, which use lean fundamentals and an optimized model-validation approach.

• Prioritization (savings: 30 percent). Models for validation are prioritized based on factors such as their importance in business decisions. Validation intensity is customized by model tiers to improve speed and efficiency. Likewise, model tiers are used to define the resource strategy and governance approach.
• Portfolio-management office and supporting tools (savings: 25 percent). Inefficiency can be reduced at each stage of the validation process, with predefined processes, tools, and governance mechanisms. These include development and submission standards as well as validation plans and playbooks.
• Testing and coding (savings: 25 percent). Automation of well-defined and repetitive validation tasks, such as standardized testing or model replication, can further lower costs.

The evolution toward capturing value systematically

To manage the P&L, capital, and regulatory challenges to their institutions’ advantage, leading banks are moving toward a robust MRM framework that deploys all available tools to capture efficiencies and value. The path to sophisticated model risk management is evolutionary—it can be usefully discussed as having three stages: building the elements of the foundation, implementing a robust MRM program, and capturing the value from it (Exhibit 1).

Building the foundational elements

The initial phase is mainly about setting up the basic infrastructure for model validation. This includes the policies for MRM objectives and scope, the models themselves, and the management of model risk through the model life cycle. Further policies determine model validation and annual review. Model inventory is also determined, based on the defined characteristics of the model to be captured and a process to identify all models and nonmodels used in the bank. Reports for internal and external stakeholders can then be generated from the inventory. It is important to note, however, that the industry still has no standard of what should be defined as a model. Since banks differ on this basic definition, there are large disparities in model-inventory statistics.

Governance and standards are also part of the MRM infrastructure. Two levels of governance are set up: one covering the steps of the model life cycle and one for the board and senior management. At this point, the MRM function will mainly consist of a small governance team and a team of validators. The governance team defines and maintains standards for model development, inventory, and validation. It also defines stakeholder roles, including skills, responsibilities, and the people who will fill them. The validation team conducts technical validation of the models. Most institutions build an MRM work-flow tool for the MRM processes.

Implementing a robust program

With foundational elements in place, banks can then build an MRM program that creates transparency for senior stakeholders on the model risk to the bank. Once model-development standards have been established, for example, the MRM program can be embedded across all development teams. Leading banks have created detailed templates for development, validation, and annual review, as well as online training modules for all stakeholders. They often use scorecards to monitor the evolution of model risk exposure across the institution.

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A fundamental objective is to ensure high-quality, prioritized submissions. Model submissions missing key components such as data, feeder models, or monitoring plans reduce efficiency and increase delivery time. Efficiency can be meaningfully enhanced if all submissions adhere to standards before the validation process begins. Models are prioritized based on their importance to the business, outcome of prior validation, and potential for regulatory scrutiny.

Gaining efficiencies and extracting value

In the mature stage, the MRM function seeks efficiencies and value, reducing the cost of managing model risk while ensuring that models are of the highest quality. In our survey of leading financial institutions, most respondents (76 percent) identified incomplete or poor quality of model submissions as the largest barrier for their validation timelines. 1 1. Many fewer respondents cited a lack of sufficient resources (14 percent) and the need to validate each model comprehensively (10 percent). Model owners need to understand the models they use, as they shall be responsible for errors in decisions based on those models.

One of the best ways to improve model quality is with a center of excellence for model development, set up as an internal service provider on a pay-per-use basis. Centers of excellence enable best-practice sharing and advanced analytics across business units, capturing enterprise-wide efficiencies. The approach increases model transparency and reduces the risk of delays, as center managers apply such tools as control dashboards and checkpoints to reduce rework.

Process automation defines MRM maturity, as model development, validation, and resource management are “industrialized” (Exhibit 2). Validation is led by a project-management office setting timelines, allocating resources, and applying model-submission standards. Models are prioritized according to their importance in business decisions. An onshore “validation factory” reviews, tests, and revises models. It can be supported by an offshore group for data validation, standards tests and sensitivity analysis, initial documentation, and review of model monitoring and reporting. The industrial approach to validation ensures that models across the organization attain the highest established standards and that the greatest value is captured in their deployment.

The standards-based approach to model inventory and validation enhances transparency around model quality. Process efficiency is also monitored, as key metrics keep track of the models in validation and the time to completion. The validation work-flow system improves the model-validation factory, whose enterprise-wide reach enables efficient resource deployment, with cross-team resource sharing and a clear view of validator capabilities and model characteristics.

Consistent standards for model planning and development allow institutions to develop more accurate models with fewer resources and in less time. In our experience, up to 15 percent of MRM resources can be conserved. Similarly, streamlining the model-validation organization can save up to 25 percent in costs. With the significant regulatory spending now being demanded of institutions on both sides of the Atlantic, these savings are not only welcome but also necessary.

The contours of a mature stage of model risk management have only lately become clear. We now know where the MRM function has to go in order to create the most value amid costly and highly consequential operations. The sooner institutions get started in building value-based MRM on an enterprise-wide basis, the sooner they will be able to get ahead of the rising costs and get the most value from their models.

Ignacio Crespo is an associate partner in McKinsey’s Madrid office, Pankaj Kumar is an associate partner in the New York office , where Peter Noteboom is a partner, and Marc Taymans is a managing partner in McKinsey’s Risk Dynamics group.

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Check 50+ Abstract Business Concepts Visualized by Icons

5 Examples of Risk Matrix PowerPoint Visualization

• February 19, 2018
• PowerPoint templates for download , Project Management & Scrum presentations , Strategy Management presentations

Last Updated on February 21, 2024 by Anastasia

One way to perform and document a risk analysis and assessment is using the risk matrix diagram. This simple visualization matrix is a management method that helps you present possible risks, and define the risk levels. As a result, you can support management decision-making and plan activities to mitigate those risks.

Explore our Business Performance PPT Reports category on the website for more resources to boost your presentation impact.

If you don’t invest in risk management, it doesn’t matter what business you’re in, it’s a risky business. Gary Cohn

Risk Matrix analysis can be easily visualized in a PowerPoint presentation. Your presentation will look more creative if you use a consistent and clear risk matrix diagram for visualization of the new company’s competitors or changes in government policy.

Remark: You can get all presented icons and slide examples in the Risk Matrix Diagram PPT set .

This visual framework is part of other management strategy tools such as SWOT, Porter Forces, and PEST. You may check this article “ 7 Visual Frameworks for Strategy Analysis Presentation ” for more examples.

Why use a Risk Matrix?

The purpose of risk management is to anticipate and control risks so as to minimize their threats and maximize their potential. The risk matrix diagram will help you to create a memorable presentation of those risks. Using a diagram illustration you can visualize with colors all risk categories and focus attention on the main subject. Risk Matrix graphics can be handy presenters who need to show risk assessment or different states of consequence process.

In this blog, we propose examples of creating and presenting risk matrix diagrams , which will help you to make the possible risks visible.

You’ll find a few variations of showing the Risk Matrix Diagram on the slide:

• showing risk types in the form of a list
• illustrating levels of probability and impact
• presenting probability and severity risk levels
• creating a risk matrix with a place for notes

Let’s start our journey over the Risk Matrix Diagram illustrations examples. See how you can show it creatively so that your audience will be focused on your presentation.

#1: Presenting Types of Risk with Creative Bullet Points

In the beginning, you may want to introduce types of risk:

• Economic risk
• Social risk
• Risk related to the use of various technological advancements
• Risk related to natural forces, so unpredictable sometimes
• Political risks covering country leaders’ actions, various lobbying on a government level, federal agencies regulations, etc.

But instead of putting those risks as a standard bullet point list, consider the example below: The first and easiest way to show various types of risk – use a simple list. This diagram includes all kinds of risks, illustrated with icons and colors. The icons for each stage will help your listeners to focus their attention on one or another point.

 #2: Illustrating Risk Probability and Impact

The next example is a typical risk matrix diagram, consisting of 4 parts. Each of them includes an eye-catching icon, therefore it can be employed for any audience: starting from students to your business partners.

#3: Adding Description to Risk Matrix

This type of risk matrix slide includes a place for the description of each point of risk. The advantage of this diagram is various symbols that illustrate emotions, so you can easily show risk analysis results.

#4: Showing Risk Probability and Severity Levels

This full-slide risk matrix diagram will help you to conduct a detailed analysis. You can identify your severity and probability risks. A colorful matrix will help you show all levels of risk:  low, medium, and high. With such a diagram template, you can easily illustrate the most dangerous risks and keep listeners attention on it.

#5: Creating Risk Matrix in Minimalist Style

If you like minimalism in your slides, then you can choose such type of risk matrix: white rectangles with colored outlines and simple icons.

Recap of four presented Risk Matrix visualizations

We presented here a bunch of ideas on how you can talk about risk without boring your audience with text-only slides. Get inspired by the examples we mentioned:

• agenda for showing all types of risk
• diagram with a minimalist style with the icons of emotions
• showing risk severity and probability in a big matrix with hand-drawn symbols
• templates for illustrating risk probability and impact

Why use diagrams for the risk matrix concept?

A risk matrix diagram is a simple mechanism to increase the visibility of risks. It is a basic management tool that is useful for strategic planning. Risk provides the objective metric to help the decision-making process. That’s why the risk matrix has been widely adopted by many businesses. The risk matrix diagram focuses on the highest-priority risks and presents complex risk data in a visual chart. 

What’s inside the Risk Matrix Template collection?

We created a collection of risk matrix diagrams in various versions and added iconic symbols for 5 types of risk. All icons are fully editable, so you can change their color and resize them without losing the image quality.

The slide set contains:

13 Matrix PPT Slide Layouts for Two-Dimensional Risk Assessment:

• Explanation of the risk matrix concept.
• Diagrams for 2×2, 3×3, and 5×5 matrices with description areas.
• 30 icons for various 5 types of risk, risk severity, and risk probability levels, in flat and handwritten styles.

All examples above are part of the infoDiagram PowerPoint Diagrams collection. It contains vector graphics that can be easily edited and added to other presentations:

Risk Matrix Diagram

Explore our YouTube channel for more creative ideas:

infodiagram

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Model Governance and Model Risk Management: Risk Manager’s Perspective

Aug 16, 2014

460 likes | 934 Views

Model Governance and Model Risk Management: Risk Manager’s Perspective. Nikolai Kukharkin Quantitative Risk Control, UBS Measuring and Controlling Model Risk, New York, October 2011. DISCLAIMER

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• simplified description
• potential action plan items
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• effective model validation
• overall valuation risk

Presentation Transcript

Model Governance and Model Risk Management: Risk Manager’s Perspective Nikolai Kukharkin Quantitative Risk Control, UBS Measuring and Controlling Model Risk, New York, October 2011 DISCLAIMER The views and opinions expressed in this presentation are those of the author and may not reflect the views and opinions of UBS and should not be cited as being those of UBS.

What Can Go Wrong With Models? • More extensive policies, stricter regulations, and more comprehensive model risk management programs.

Kill All the Quants?... * • Risky Business on Wall Street: High-tech supernerds are playing dangerous games with your money TIME magazine, April 11, 1994 • Recipe for Disaster: The Formula That Killed Wall Street Wired Magazine, Feb 23, 2009 • The Minds Behind the Meltdown: How a swashbuckling breed of mathematicians and computer scientists nearly destroyed Wall Street WSJ, Jan 22, 2010 • Financial Crisis Can Be Traced to “the Quants” The Kansas City Star, Feb 22, 2010 *) Andrew W. Lo, “Kill All the Quants?: Models vs. Mania in the Current Financial Crisis”, 2009.

…Before They Are Born… Too large a proportion of recent "mathematical“ economics are mere concoctions, as imprecise as the initial assumptions they rest on, which allow the author to lose sight of the complexities and interdependencies of the real world in a maze of pretentious and unhelpful symbols. John Maynard Keynes, The General Theory of Employment, Interest and Money, 1935

…Or May Be Not? Myron Scholes, Risk Magazine, September 2011 • It should be a golden age for risk modeling and management • “One thing about a crisis is that it shakes old opinions and you start learning new things. I hope we do – I am very bullish on the future for quants.” • He warns against overreliance on models, and concedes they had a role in the crisis. But the common-sense reaction – embracing intuition, and rejecting the use of modeling and quantitative techniques – is also flawed, he argues… • Presumably you used your intuition in picking the model, and intuition can fail, too.

New FED/OCC Supervisory Guidance on Model Risk Management • Expands on existing regulatory guidance by broadening the scope beyond model validation to include all aspects of model risk management at all stages: model development, implementation, and use • Revises and expands model and model risk definition • Establishes comprehensive model risk management program requirements • More formalized and expanded model governance and controls • Increased standing of model risk management function: needs to be influential; have explicit authority to challenge model developers and users • Model validation • Introduces “effective challenge” standard • Key elements of comprehensive validation : • Evaluation of conceptual soundness • On-going monitoring • Outcomes analysis

Comprehensive Model Risk Management Program Requirements • The bar has been raised significantly with respect to the scope, formality, rigor, and prominence expected of banks’ model risk management programs. • “Model risk should be managed like other types of risk.” • Life-cycle view of model risk - model risk management framework is expected to include standards for model development, use, and maintenance to which all model owners, users, and other stakeholders will be held. • Broader roles and responsibilities – it is not just the responsibility of the model validation unit: model developers / owners, users, validators, senior management, internal audit • Model risk management is an on-going, continuous, process – not a periodic activity: • Monitoring model risks and limitations identified during development and validation • Monitoring and on-going validation of changes (i.e., products, exposures, activities, clients, or market conditions) that may impact model risks • Regular model performance monitoring (i.e., back-testing, benchmarking, sensitivity analysis, and stress testing) • Model risk reporting to senior management and the board of directors.

How Should Banks Respond? • Examiners expect a bank to perform a self-assessment against new regulatory guidance, and have a clear action plan for closing identified gaps. • Potential action plan items may include the following: • Revisions to policies and procedures • Revisions to roles and responsibilities • Organizational changes • Development of new standards and guidelines for model development, implementation, and use • Revised model inventories (including an inventory of model-specific risks and limitations) • Mappings of model risk mitigation controls against existing inventory of model risks and limitations • Creation / enhancement of on-going model monitoring processes • Creation / enhancement of model risk reporting • Additional model validation testing (e.g., vendor models) • Creation of annual model review process

Model Validation: What’s Next? • Financial industry obtains a significant share of revenue from products valued by mathematical models • Models are here to stay and reliance on them will only grow • Consequently, model risk is a topic of great, and growing, interest in the risk management arena • How to define it • How to measure it • How to manage it • Qualitatively reasonably well defined • Much less successfully quantified and even less successfully managed • What is expected from model validators and how is the role changing? • What are the key priorities for model validators?

Model Risk: Define and Manage A model can be defined as: A simplified description or representation of an entity or process, property, characteristic or behavior which cannot be represented or predicted with complete certainty.The output of a model is therefore an estimate or approximation. 1. Model risk is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. 2. Model risk is the risk of error due to a deficiency in design or implementation of a pricing model. In other words, model risk is the risk of occurrence of a significant difference between the mark-to-model value of a product and its fair value. Or more flexible definition by R. Rebonato 3. Model Risk is the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market. Note that neither “true” nor “fair” value is mentioned, i.e. ->>> Market is the king More sophisticated / realistic / correct model is not necessarily the best

Model Governance Process • Regulators on Model Risk: FSA “Model Risk contributes to overall valuation risk. Model validation and model risk management processes are important elements of any valuation control framework. Whilst effective model validation is fundamental, model validation, however good, does not remove model risk. Few firms have sufficiently well developed frameworks for articulating model risk tolerance, and measuring and controlling model risks within that tolerance. We believe a better defined and implemented model risk management framework could therefore feed into a better defined and implemented valuation risk-management framework.” - from FSA’s “Dear CEO” letter on Valuation and Product Control principles, August 2008 • Regulators on Model Risk: FED and OCC Supervisory Guidance on Model Risk Management, April 2011 Model risk should be managed like other types of risk. Banks should identify the sources of risk and assess the magnitude. Model risk increases with greater model complexity, higher uncertainty about inputs and assumptions, broader use, and larger potential impact. …With an understanding of the source and magnitude of model risk in place, the next step is to manage it properly.

Model Risk Management Framework Goal is to set up a framework to explicitly, fully, and dynamically account for model risk • Partially accounted for by: • Qualitative capture • Models certification • Periodic model risk review • Quantitative capture • Model reserves • Sensitivity analysis • Portfolio reviews

Model Risk Management Framework Governance: Independent Verification Unit (IVU) with the mandate for: - independent review and certification of valuation models - independent risk-based review of model-related risks, i.e., the risk that the model either through a deficiency in design or implementation produces faulty output. The aim of the certification process is to obtain the required level of comfort that the model in question is functioning in an accurate and appropriate way. Model performance monitoring process: - Full review of potential model risks and existing control processes across systems and product areas taking into account factors including materiality, model choice, model applicability - This is independent from running certification processes, it challenges existing model issues and includes actions that are or should be taken Frequency and depth of reviews: Regulators requested model verification updates of the “high-risk” to be done more frequently, annually, compared to previous 5-y cycle for all certifications.

Model Governance Process – High-Risk Models Definition of High-Risk Models Define dimensions and criteria to categorize models as “high risk” Definition is altered based on new information / past actions Capture of High-Risk Models Agree on review types Run / participate in the reviews throughout the year Review Filter all models according to preliminary criteria Working Groups of IVU product specialists adjust filtered product list and define necessary actions Actions based on Reviews Consolidate results of reviews already undertaken Take further actions if necessary

Definition - Model Risk Types The following major model risks types were defined 1. Model inconsistencies or approximations Inconsistencies in mathematical assumptions of the model or its implementation Model assumed fit for purpose, although does not fully capture some features The model may be used outside of its range of applicability 2. Model choice Model choice uncertainty - several models are available (and one is being used) Many solutions could satisfy the same constraints 3. Calibration, model parameters, and input data issues Multiple sets of parameters can satisfy market; multiple sets of calibration instruments available, sometimes model cannot fit all of them simultaneously; uncertainty in model input parameters 4. Controls (booking approximations + level of oversight) The control environment into which the model is being released. Level of oversight by other control groups and therefore the probability of an error being detected 5. Complexity Exotic features, number of inputs, and the importance of inter-relationships between them models assumptions/conditions 6. Model/Product maturity and level of standardization Maturity, liquidity and rate of change of the market

Capture – Model Risk Scores Model risk scoring process Start with the certified product list and link and rank it according to the risk scores in several dimensions as well as materiality of positions: • Each product is described in terms of “High=3”, “Medium=2” or “Low=1” Risk Scores for each of the following six risk factors (combined Risk Score is between 6 and 18): 1. Model inconsistencies or approximations 2. Model Choice 3. Calibration, model parameters, and input data issues 4. Controls (booking approximations + level of oversight) 5. Complexity (exotic features, number of inputs, models assumptions/conditions) 6. Model/product maturity and level of standardization • Capability to compare model risk between models / products across all areas • This list is used as a starting point to identify models/products which will be subject to the annual review • Additional win – not just a formal more frequent re-certification, but rather a review targeting specific features which make this product/model high-risk

Is Theoretical Value Fair? • Most of the issues listed in the previous section can also give rise to model fair value adjustments Fair value is the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date (FAS 157) • Accounting standards demand that if the value of asset or liability is not directly observable but rather obtained from a model (“marked-to-model"), it needs to be further adjusted to bring it into closer alignment with the market fair value. Why is such adjustment needed? • Model can have a known bias/deficiency • There can be an uncertainty around the model generated value due to: • existence of the alternative models (i.e., no industry standard) • non-uniqueness of calibration • uncertainty around (unobservable) model inputs • Terms “Model Fair Value adjustments” and “Model reserves” are often used interchangeably, but they mean different things and should not be mixed up

What Do Accounting Standards Say About Model Reserves? They say… NOTHING Model reserves may serve as a proxy, intuitive way to account for perceived model risk Model FV adjustments aim at “fine-tuning” model generated number to bring it into a better alignment with the market price. FV adjustments make the “best educated guess” of where the market is Model risk attempts to assess the tails of the theoretical price distribution, expresses how far off our “best guess” might be from the realized price • Model risk arises from the uncertainty in model specification, be it the model parameters and/or inputs (i.e., function arguments), or the model (function) itself. When not observable, FV of an asset is a variable characterized by some probability distribution. While model FV adjustments attempt to pinpoint the center of such distribution, its higher moments are the domain of model risk. • NOTE: Frequent practice of using “parameter uncertainty” and sometimes “alternative model” reserves to create a “conservative cushion” roughly the size of perceived model risk, contradicts the accounting standards which concentrate on fair value.

Inherent Model Risk • Valuation uncertainty beyond model FV adjustments is the domain of (inherent) model risk • Can be viewed as “residual” model risk. With proper model validation in place, inherent model risk can be minimized but never eliminated • Needs to be measured, monitored, and managed • Deserves a place in overall risk management framework on a par with market and credit risk • Should be considered alongside the market and credit risks in allocating capital, making business decisions, and managing the trading positions

Final Observations • There is always a risk that a model can be “wrong” • In part model risk is a variety of operational risk, i.e. the possibility of a human error • However, there is also an inherent uncertainty due to the very nature of financial modeling • The purpose of model governance is to set up policies and procedures that: • 1) minimize operational risk - Achieved through model validation, periodic reviews, model change management, back-testing, etc. • 2) provide for measurement, monitoring, and management of inherent model risk (model uncertainty) - Requires recognition of model uncertainty’s role alongside market and credit risk, and devising and implementing methods, processes and systems for measuring capturing reporting and managing model risk • Therefore, model risk deserves a place alongside market and credit risk in making business decisions (e.g., in capital allocation) as well as in the risk management and reporting process.

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• Systematic Review
• Open access
• Published: 19 June 2024

Predictive models of Alzheimer’s disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal

• Xiaotong Wang 1 ,
• Shi Zhou 1 ,
• Niansi Ye 1 ,
• Yucan Li 1 ,
• Pengjun Zhou 1 ,
• Gao Chen 1 &
• Hui Hu 1 , 2 , 3  

BMC Geriatrics volume  24 , Article number:  531 ( 2024 ) Cite this article

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Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer’s disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown.

The objective of this review was to provide an overview of prediction models for the risk of Alzheimer’s disease dementia in older adults with mild cognitive impairment.

PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer’s disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I 2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias.

The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90).

Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer’s disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results.

Registration

This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).

Peer Review reports

Introduction

According to the WHO, more than 55 million people worldwide (8.1% of women and 5.4% of men over 65) are currently estimated to be living with dementia. This number is estimated to increase to 78 million by 2030 and 139 million by 2050 [ 1 ].So the World Health Organization approved “the Global Action Plan on the Public Health Response to Dementia 2017–2025” at the World Health Assembly in May 2017, which proposes strategies for the prevention and treatment of dementia and provides guidance on improving the quality of life of people living with dementia, their families and caregivers [ 2 ]. Alzheimer’s disease (AD) is the most common cause of dementia, accounting for 60–80% of dementia. It has been reported that every patient with AD dementia experiences mild cognitive impairment (MCI), a stage considered to be the transition between normal ageing and dementia. Statistics show that the prevalence of MCI is about 8% in people aged 65 to 69 years, rising to 15% in people aged 70 to 79 years, 25% in people aged 80 to 84 years, and 37% in people aged 85 years and older [ 3 ], however, because the current diagnostic and treatment model of cognitive impairment in the elderly has not yet been perfected, coupled with the lack of awareness of the importance of treatment among patients and their families as well as uncertainty about the effectiveness of MCI treatment [ 4 ], Existing statistics may significantly underestimate the actual prevalence of MCI in older adults, and these factors also highlight the challenges faced in early screening and intervention services for AD dementia. It has been suggested that without intervention at the MCI stage, about 15% of people with MCI will develop AD dementia after two years [ 5 ]. However, effective intervention at this stage can delay cognitive decline [ 6 ]. In the face of the challenges of global ageing, screening and treatment of older adults with MCI should receive more attention, and dementia risk prediction is crucial for identifying at-risk populations.

Several studies have indicated that risk prediction models can assist healthcare professionals in identifying patients who are at high risk of cognitive decline. For instance, Wang et al. [ 7 ]. developed a predictive model to assess the risk of MCI in normal community-dwelling older adults. Another meta-study by Gopisankar et al. [ 8 ]. analyzed risk factors for MCI in Chinese older adults and developed a new hybrid model by updating and evaluating three existing models and applying a deep neural network analysis. This new model demonstrated higher predictive performance in assessing the incidence of dementia. An et al. [ 9 ]. conducted a meta-analysis and identified critical indicators of objectively measured cognitive impairment in individuals who have reported experiencing subjective cognitive deterioration. They also developed risk prediction models under two scenarios to identify individuals more likely to experience clinical progression.

However, the prediction models themselves have generated some controversial discussions, such as a meta-analysis by Huang et al. [ 10 ] to assess the predictive performance of multivariable prediction models for cognitive decline in older adults, which showed that the usefulness of the models was not very high. Li et al. [ 11 ] found that the prediction of individual disease risk varied significantly between different types of machine learning and statistical models with almost the same level of discrimination. However, the different views generated by the above studies are mainly caused by the significant differences in the existing models regarding data sources, sample sizes, and data processing and analysis methods. At present, there is yet to be a consensus on the most effective model. Considering that filling this research gap will be the key to predicting the progression of the disease in older adults with MCI and the implementation of timely and effective diagnosis and treatment, the present study will comprehensively analyze the published dementia-related risk prediction models.

This systematic review and critical evaluation was carried out to: 1) Provide a comprehensive summary of the best-performing multivariable predictive models in all current studies,2) Summarise models relevant data and methodological issues in model development and validation for 16 studies, and 3) Explore whether machine learning and traditional modelling approaches may affect model performance. The outcomes of this study aim to enhance the dependability and precision of AD dementia risk prediction models, thereby informing future efforts in predictive modeling and validation.

This study strictly followed the CHARMS checklist for systematically evaluating predictive modelling studies [ 12 ]. PROBAST [ 13 , 14 ] was used to assess the bias and applicability of the predictive modelling studies. We used various methods to obtain estimates and confidence intervals for each study’s optimal model performance measures. For data extraction, we prioritized the performance statistics that were derived using the most convincing validation methods (in increasing order of confidence: external validation, i.e., evaluation in independent populations, internal validation such as Bootstrap validation, cross-validation, random training-test splits and time splits, and the same data as in the development process). Performance statistics (in increasing order of confidence: external validation, i.e., evaluation in an independent population; internal validation, such as Bootstrap validation, cross-validation, randomized training-test splits and time splits, and evaluation with the same data as in the development process) [ 15 ], and finally we merged and summarized the data.

Literature search

Our comprehensive search across PubMed, EMBASE, Web of Science (WOS), and MEDLINE, spanning from each database’s start to October 19, 2023, focused on identifying models designed to estimate the likelihood of AD dementia in individuals 60 years and above who have MCI. Furthermore, we retrieve by using search filters that recognize predictive modelling studies [ 16 ]. The filters have been validated to have high sensitivity in retrieving clinical prediction model studies. A comprehensive inventory of the search terms utilized is available in Appendix File 1 .

Eligibility criteria

This analysis encompassed all primary research that either created or confirmed multivariable prediction models (with a minimum of two predictors). A thorough delineation of the study’s population, the primary model being evaluated, the model used for comparison, the outcomes, the time frame, and the context (PICOTS) is depicted in Table  1 .

The literature inclusion criteria were as follows:

Study population: older adults with MCI with a mean age of 60 years or older.

Study content: Studies on risk prediction models for the progression of MCI to AD dementia.

Study type: cross-sectional surveys, case-control studies, and cohort studies.

Outcome metrics: compliance with the diagnostic criteria for AD(includes the NINCDS-ADRDA [ 17 ]diagnostic criteria, any version“Diagnostic and Statistical Manual of Mental Disorders” [ 18 ], NIA [ 19 ], IWG [ 20 ], standardized neuropsychological tests and clinician’s diagnosis or a combination of these criteria.

Literature exclusion criteria were as follows:

Studies on specific populations, such as some specific diseases (e.g., organic diseases such as stroke, epilepsy, and Parkinson’s).

The number of predictors was less than 2.

Duplicate publications.

Unofficial publications such as conference abstracts, academic papers, and so on.

The language of the article needed to be English.

Screening process

Screening was performed using Endnote X9 software. Initially, two independent reviewers (XW, PZ) screened titles and abstracts for predictive modelling studies based on inclusion and exclusion criteria, with a third reviewer (NY) participating when necessary. After consensus was reached, the full-text literature was independently searched and screened by two reviewers (XW, PZ); in addition, we conducted a manual examination of the reference lists in the selected studies to identify additional studies that might be pertinent [ 21 ].

Data extraction

In this scientific investigation, the data extraction was carried out independently by two researchers, XW and PZ. Use CHARMS [ 12 ] to design standardized data extraction forms. The critical information extracted followed the principles of PICOTS, i.e., number of subjects included, data source, predictors (e.g., patient characteristics, imaging or biological markers), model status (e.g., performance, modelling status, and model presentation), and outcome metrics (e.g., measurement tools for AD dementia and duration of follow-up). In addition, information including author names, year of publication, type of study, and statistical information (e.g., treatment of missing data and selection of predictors, treatment of continuous variables) was also collected. Finally, we calculated the minimum sample size by pmsampsize package in R. We reviewed the supplementary material of the articles to ensure that all information about the articles could be extracted in its entirety. (see Appendix Table  6 ).

In order to analyze the predictive ability of each model, this study intends to analyze the following metrics: Confusion Matrix, Accuracy, Sensitivity, Specificity, Precision, F1 Score, AUC, DCA to evaluate the clinical applicability of the model were extracted.1) Discrimination, which refers to the model’s ability to discriminate between people with AD dementia and those without AD correctly, is often measured by the Consistency Statistics (C-index) and the AUC. The AUC is a measure of the accuracy of the model, the closer the AUC is to 1, the better the diagnostic effectiveness of the model [ 11 ]; 2) Calibration, the degree of accuracy of probability prediction is called “calibration degree”, is a way to measure the size of the difference between the probability predicted by the algorithm and the actual result, also called consistency, goodness-of-fit, mainly through the Hosmer-Lemeshow test and the goodness-of-fit curve evaluation [ 21 ]; 3)Clinical validity evaluation metrics, DCA, this approach serves to assess clinical predictive models, diagnostic examinations, and molecular markers. It aligns with the practical demands of clinical decision-making processes, often being more prevalent in external validations [ 22 ]. In addition to the above several conventional metrics, include the Confusion Matrix, Accuracy, Sensitivity, Specificity, F1-Score, and Brier Score [ 23 ]. (see Appendix Table  7 )

If inconsistencies are found during the data extraction process, NY will adjudicate these inconsistencies. Our study was guided by a systematic review and meta-analysis of preferred reporting items (TRIPOD-SRMA). For a complete list of search terms used.

Risk of bias assessment

This review applies the Risk of Bias Assessment Tool (PROBAST) [ 14 ] to assess the risk of bias (ROB) and the applicability of prediction models. PROBAST consists of four domains: participants, predictors, outcomes and analysis. Each question can be answered as “yes”, “probably yes”, “probably no”, “no”, or “no information”, As long as one of the domains is answered “no” or “probably not”, the domain is considered high risk; as long as the question in each domain is answered “yes” or “probably yes”, it will be defined as low risk. The overall ROB was deemed low when each domain consistently exhibited a low ROB. Conversely, the ROB was categorized as unclear, in cases where one or several domains exhibit an uncertain ROB, while the remaining domains are assessed as low in ROB. The applicability evaluation was similar to that of ROB, but only the first three domains were used to evaluate the applicability of the predictive model. The first two researchers (XW and PZ) assessed independently, and finally, the third reviewer, NY, made the judgment.

Statistical analysis

We used the R version 4.3.1 for the meta-analysis. Different from the conventional Meta-analysis, due to the significant heterogeneity of the predictive models, we directly used the random effects model [ 13 , 22 ], the AUC of the models and the calculated (approximate) 95% prediction intervals were combined and estimated, heterogeneity was quantified using the I 2 statistic, where p  < 0.05 and I 2  > 50% signify statistically significant heterogeneity [ 23 ]. This statistic indicates the extent of variation across studies attributable to heterogeneity. To further analyze this variation, we divided the studies into two subgroups: those using machine learning modelling and those employing traditional modeling. Additionally, we utilized a funnel plot to illustrate the risk of bias.

Selection process

The PRISMA flowchart illustrates our process for searching and selecting literature. In our search, we gathered a total of 3,337 potentially relevant records, sourced from PubMed (2,920 records), EMBASE (1,461 records), Medline (1,910 records), Web of Science (2,979 records), and manual searches (12 records). We removed 3,563 records identified as duplicates, leaving 5,626 unique records for initial review based on titles and abstracts. Of these, 5,579 were subsequently excluded from title and abstract evaluation. Ultimately, we thoroughly reviewed 43 full-text articles, and 16 of these met our criteria for inclusion in this study. The literature screening process is shown in Fig.  1 .

Systematic reviews and Meta-Analyses (PRISMA) flowchart of literature searching and selection

Summary of findings

Study designs and population.

All prediction models ( n  = 16) were development models. The majority ( n  = 13) were retrospective cohort studies, and three [ 24 , 25 , 26 ] were prospective cohort studies. One study [ 24 ] was from a medical examination centre. One study [ 25 ] was from a memory clinic. One study [ 26 ] was recruited from a community-based primary health care centre, two [ 27 , 28 ] were multicenter retrospective cohort studies, and 11 studies [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ] were from the ADNI database in North America. In addition, there was one study [ 29 ] from the NACC (The National Alzheimer’s Coordinating Center) in the United States, two studies [ 24 , 26 ] from China, and two other studies from Korea [ 25 ] and Spain [ 30 ] respectively. In our prediction modelling, the populations studied varied in size, ranging from 102 to 2,611 individuals, and the prevalence of MCI progressing to AD dementia ranged from 15.03 to 52.22%. Detailed characteristics are shown in Appendix Table  6 .

We identified more than 400 candidate predictors and 94 final variables in our predictive model, divided into four main types: demographic characteristics, health-related risk factors, cognitive scores, and various biomarkers. The following 16 predictors were used at least twice as predictor variables in the model: MMSE, age, FAQ, ADAS, ApoE4, education level, hippocampal volume, CDR, AVLT, gender, p-tau, Aβ amyloid, cortical thickness, and ADL, age ( n  = 10, 62%), MMSE ( n  = 10, 62%), and FAQ ( n  = 62, 14%) were the most common predictors. (See Fig.  2 .)

: An overview of the most commonly used predictors in AD dementia risk prediction models. *MMSE: Mini-Mental State Examination; FAQ: Functional Activities Questionnaire; ADAS, Alzheimer’s disease assessment scale-cognitive subscale; APOE4, Apolipoprotein E 4 allele; p-tau: Highly phosphorylated tau protein; CDR, Clinical dementia rating; ADL: Activity of daily living

Missing data and continuous variables

Three studies used an entire case study approach [ 24 , 27 , 31 ], one study used median imputation [ 32 ], one study used machine learning for imputation [ 39 ], one study [ 29 ] directly deleted missing data, and the remaining ten studies [ 25 , 26 , 30 , 31 , 34 , 35 , 36 , 37 , 38 ]did not indicate how the missing values were handled. Four studies [ 24 , 26 , 30 , 32 ] converted continuous variables to categorical variables, six studies [ 25 , 27 , 28 , 36 , 37 , 38 ] retained continuous variables, and the remaining five studies [ 29 , 31 , 33 , 34 , 35 , 39 ] did not indicate how the variable transformation was performed. Regarding the choice of variable screening methods, two models [ 35 , 39 ] chose LASSO to screen variables, two studies [ 30 , 38 ] chose RF for screening, and the other studies ( n  = 10) chose COX regression, complete subset regression, and MRMR, in addition to one study [ 28 ] that did not specify the screening method.

Modelling method and follow-up duration

Most of the forecasting models ( n  = 11) [ 24 , 25 , 26 , 28 , 30 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ] were developed using machine learning, two studies [ 27 , 31 ] modeled Cox proportional risk regression models, two studies [ 29 , 32 ]combined a mixture of modeling approaches for hybrid modeling, and one study [ 28 ] did not indicate the model type. The forecast period was 2–7 years, most studies [ 25 , 26 , 27 , 28 , 31 , 32 , 35 , 36 , 37 , 38 , 39 ] ( n  = 9) predicted the AD dementia time for medium-term forecast (3–5 years), two models [ 24 , 34 ] focused on the short-term forecast (1–2 years), and 3 models [ 29 , 30 , 32 ] were for the long term forecast (5–10 years).

Model performance and validation

Two models have been externally validated [ 32 , 35 ], and the rest have only been internally validated. Among the internally validated models, one model [ 38 ]used random partitioning, one model [ 31 ] used bootstrapping, and eight models [ 24 , 25 , 26 , 27 , 30 , 34 , 35 , 36 , 37 ] used cross-validation, of which three models [ 28 , 32 , 37 ]used hierarchically nested cross-validation, and two models [ 29 , 33 ] used combinatorial methods. Regarding model performance, all models reported discrimination except for one model [ 39 ], which did not provide discrimination. Fifteen models had an AUC greater than 0.70, and one model [ 29 ] had an AUC ranging from 0.50 ? ∼  0.69. In addition, eight models [ 25 , 27 , 28 , 30 , 31 , 34 , 36 , 37 , 38 ] reported calibration or predictive model fit goodness-of-fit curves, and all reported models were calibrated. The sensitivity of the models was 0.62 ? ∼  1, and the specificity was 0.69 ? ∼  0.98. Accuracy ranged from 0.50 to 0.99%. (See appendix Table 7 )

Model presentation

Five models [ 25 , 28 , 30 , 31 , 35 , 39 ] were presented as scoring systems, one model [ 34 ] was presented as an equation formula, one model [ 38 ] was presented as a graphical scoring method, one model [ 27 ] was presented as a web-based calculator and an app, one model [ 32 ] was presented as a column-line graph, and the other six models [ 24 , 26 , 29 , 33 , 36 , 37 ] were not reported.

Risk of bias and applicability

Fourteen models were rated high, and two [ 25 , 34 ] had an unclear ROB (see Appendix Tables  8 and 9 ). We found a model with a low risk of bias, but without external validation [ 29 ]. Therefore, we classified them as high ROB. Two models [ 30 , 32 ] were judged to have high ROB in the participant, mainly because the study population did not represent the model’s target population. Most models were judged to have unclear ROB in the outcome, mainly due to some interference between the outcome measures and perhaps the predictors. Four models [ 24 , 27 , 28 , 37 ] had high ROBs in the analysis domain related to insufficient sample size, and nine models [ 25 , 26 , 29 , 31 , 33 , 35 , 37 , 38 , 39 ] rated as unclear were mainly related to the unclear on the handling of variables.

Regarding applicability, five models [ 31 , 33 , 36 , 37 , 39 ] were rated as having an unclear concern of applicability in the domain of participants. Two models [ 29 , 30 ] were highly biased in the outcome domain as long as they were related to a mismatch between the outcome indicator and the systematically evaluated question. This implies that the settings or participant demographics in these predictive modeling studies might not align perfectly with the context of our research question. Overall, most of the model ( n  = 14, 85%) had a high ROB, and about half of the models ( n  = 7, 43%) had a concern of applicability that was unclear or high.

Meta-analysis of validation models

We performed a meta-analysis including 15 development models reporting AUC and their 95% confidence intervals (95% CI). The Li et al. [ 39 ] study was excluded due to missing AUC. Pooled analysis of the 15 studies showed that we combined all AUCs using a random-effects model, with an AUC of 0.86 (95% CI: 0.82, 0.90) and an I 2 of 95% ( p  < 0.01) (Fig.  3 .), suggesting high heterogeneity.

In addition, we conducted subgroup analyses by grouping the traditional regression model, and ML showed that the effect size of the traditional regression model subgroup (0.89; 95% CI: 0.86,0.93) was more significant than that of the traditional regression model subgroup (0.77; 95% CI: 0.71,0.74) (Fig.  4 .), and the results suggest that different modelling may be a potential heterogeneity of study results Reason. Publication bias was evaluated using funnel plots (see Appendix Fig.  3 ). The distribution of the scatter plot appeared largely symmetric, indicating an absence of notable publication bias in the prediction models analyzed.

Forest plot of meta-analysis of pooled AUC estimates for 15 validation models. *95% CI, 95% confidence interval; ML: machine learning

Forest plot subgroup analysis of pooled AUC estimates for 15 validation models. *95% CI, 95% confidence interval; ML: machine learning

Principal findings

This research offers a comprehensive analysis of predictive models aimed at determining the risk of AD dementia in elderly with MCI, examining 16 different models. These models, formulated in both community and clinical environments, primarily target the elderly population, including those attending memory clinics. Notably, Five studies opted for Random Forest (RF) as the primary modeling tool during model construction. This choice was primarily due to RF’s ability to robustly handle high-dimensional data and demonstrate strong generalization capabilities [ 40 ]. Additionally, two studies employed the Cox Proportional Hazards Model (COX), exhibiting exceptional survival analysis performance [ 28 ]. Researchers also favored fusion models as they combine the strengths of multiple models, significantly enhancing prediction accuracy [ 29 , 32 ]. Support Vector Machine (SVM) was often used in some studies to classify data [ 33 ]. Cyclic Neural Networks (RNN) demonstrated unique advantages in capturing time dependencies, while Artificial Neural Networks (ANN) stood out for their powerful learning capabilities [ 41 ]. When dealing with large-scale datasets, eXtreme Gradient Boosting (XGBoost) exhibited efficiency, and the Variational Bayes (VB) method provided valuable uncertainty estimations [ 25 ]. Each modeling approach possesses unique advantages and different application scenarios.

Then, we conducted a subgroup analysis of ML versus traditional modelling approaches and found that ML algorithms were more effective than traditional regression models in outcome prediction. However, it has been argued [ 42 ] that the reliance on large amounts of data for machine-learning approaches may limit their effectiveness in small sample datasets. Reinke et al. [ 43 ] compared classical and ML approaches to develop dementia risk prediction models and found that ML did not outperform logistic regression, confirming the importance of sample size. This also shows that the data and features determine the upper bound of the predictive model, and the model and algorithm only help the research to keep approaching the upper limit.

Furthermore, by summarizing the most used predictors in prediction models for the stage from MCI to AD dementia, our findings are inconsistent with An et al. [ 9 ], mainly due to inconsistencies in the study population. Although the close association of cognitive biomarkers with the pathology of the disease makes them overall superior to epidemiology and neuropsychology, we observed that four of the top five predictors were non-cognitive biomarkers. Only ApoE4 was a cognitive biomarker, which may result from some scholars’ preference to use relatively valid demographic characteristics and neuropsychological scales due to cost considerations as predictor variables. Given this, we suggest that future studies develop more diverse predictive models based on different clinical settings, community environments, and individual circumstances.

Most of the studies were developing new models, and our assessment of them found that the predictive power of the models ranged from moderate to excellent. However, these models consistently have a high or unclear ROB. With three models [ 26 , 28 , 31 ] showing low ROB in all but the analysis, suggesting that the models performed well in terms of study design and data collection but had problems in terms of statistical analysis, commonly such as insufficient sample size, insufficient consideration of model overfitting issues, use of missing data and unclear treatment of continuous variables [ 44 ], it is likely to result in good performance on the training set but poor performance on the test set or in real-world applications. This means that while the model learns the features of the training data well, it cannot generalize to new data effectively, dramatically reducing the model’s usefulness and reliability [ 45 ]. Although predictive modelling holds some promise for improving AD dementia prevention and intervention, given the insufficient evidence, it is yet to be possible to recommend any predictive model for widespread use in practice.

Challenges and opportunities

This comprehensive analysis identified certain methodological shortcomings in the integrated development or validation of predictive models.

First, although many models have been internally validated and calibrated, only a few have been externally validated. It is worth noting that predictive models usually outperform external validation in model development data, but external validation is more convincing than internal validation [ 46 ]. Therefore, to ensure the generalizability of the models, we emphasize the importance of using different datasets as much as possible to validate the existing model’s performance. In validation studies, we need to verify that the model’s performance (discrimination and calibration, especially discrimination) on new data is close to the performance on the data on which it was developed [ 47 ], and the assessment of model usefulness requires a clinical judgment; furthermore, in machine learning the performance of a model may undergo the concept drift, over time, and thus continuous validation and updating of the predictive model is necessary to ensure applicability to new populations.

In addition, we found that about half of the models suffered from direct deletion of missing values and incomplete reporting. Failure to treat missing data appropriately usually leads to biased effect estimates because missing data can distort the performance of a predictive model if it is correlated with other variables [ 48 ]. Missing value imputation methods are categorized into deletion, simple imputation, multiple imputation, and algorithmic imputation, while multiple imputation [ 49 ] and Miss Forest [ 50 ] are currently the more recommended methods. About half of the studies transformed continuous variables into binary or multi-class classification. However, many researchers have hotly debated the treatment of continuous-type variables. From a statistical point of view, downgrading continuous variables to categorical variables, especially binary classification is highly likely to result in the loss of data information and reduced prediction performance. However, from the clinical point of view, it is easier to quickly determine the outcome of the patients [ 51 , 52 , 53 ]. Therefore, which method should be taken should be considered according to the purpose of the study, the method used, and the data.

In addition, we note that different methods were used to screen the model’s predictor variables. If the number of variables is too high, the model may overfit the training data, decreasing predictive performance on new data, and too few may lead to poor model performance [ 54 ]. Thus, it is essential to ensure that the model captures the key features of the data while keeping the model concise. There are three general categories of variable selection methods: filter, wrapper, and embedding [ 55 ]. The most appropriate method for selecting predictors still needs to be discovered. However, for data with many features (or data that exhibit multicollinearity), it is expected to use regularized regression (often also known as penalized models or shrinkage methods) to impose restrictions and thus reduce the occurrence of overfitting. In addition, the sample size is also closely related to the variables. In addition to the traditional 10-EPV estimation method, sample size calculation tools have been developed to estimate the sample size of clinical prediction models [ 56 ].

Furthermore, the practical application of risk models should take into account their cost-effectiveness. Typically, models that incorporate high-cost predictors tend to exhibit greater predictive accuracy compared to solely depending on the judgment of clinicians [ 57 , 58 ]. However, model feasibility and cost constraints can limit model use, particularly in primary care [ 59 ]. Model simplicity and measurement reliability are essential for developing clinically useful prognostic models. The current study shows that clinical judgment frequently demonstrates comparable or superior performance compared to predictive models, and some predictors may instead limit the use of these models due to their invasive nature, the high cost of the tests, and the non-routine nature of the tests [ 60 ]. Therefore, we suggest that future studies need to consult with clinical experts on the one hand for their opinions and insights, including the interpretability and applicability of these variables in actual clinical settings, in addition, continuously updating current models by mining predictors with more vital incremental value (exploring non-cognitive biomarkers with modifiable properties and more common biomarkers) to identify patients at high risk of AD dementia more effectively.

Finally, we found that the included studies needed more model presentation, incomplete regression equations and lack of clarity about the intended use of the model used. An inadequate presentation of research studies not only represents a significant squandering of research resources but also obstructs future activities such as validation, updating, recalibration, and providing direction for clinical practice. In terms of model presentation, in addition to providing complete model equations, there are many forms, such as scoring systems, column-line graphs, web calculators, and apps. In addition, Bonnett et al. [ 61 ] point out that even if models do not perform very well, they may still be of clinical utility. Therefore, indicating that the specific intended use of a predictive model (i.e., when or where they will be used in an investigation and for whom they will be used) may be equally relevant may be helpful.

Advantages and limitations

This study represents the first comprehensive and integrated assessment of AD dementia risk prediction models for elderly with MCI. Critical features were assembled through an extensive literature search, meticulous screening, and standardized data extraction, thus providing valuable research information for primary healthcare systems and clinical healthcare professionals. This approach lays the foundation for more effective construction and external validation of future predictive models. Furthermore, this study conducted a risk of bias (ROB) assessment and applicability assessment of the prediction model using the PROBAST tool, alongside subgroup and bias analyses, constituting another significant strength in our study.

However, this review is subject to certain limitations. First, despite identifying multiple models that predict conditions in similar populations, a comprehensive meta-analysis of the discrimination and calibration of these models was not feasible due to the lack of detailed calibration reports. Second, the “best model” selection might have overlooked some essential information due to incomplete data extraction. Additionally, despite using random-effects models, there was still a high heterogeneity among the included studies, mainly because there were also unadjustable differences between the patient environments used for study design, measurement methods, and patient characteristics. Heterogeneity is also usually accepted in Meta-analyses of predictive models, but future subgroup analyses of its variability in relevant settings and populations by meta are necessary.

Future research

To help make clinical decisions founded on the most robust available evidence and to identify the most effective models advocated or utilized for predicting the risk of AD dementia in older adults with MCI. However, we are unable to recommend any specific model due to several reasons. Firstly, nearly all predictive models reviewed exhibited unclear or high risk of bias (ROBs), and the included developmental models required further external validation. Additionally, the significant heterogeneity among the included models, the use of non-standardized statistical analysis methods, incomplete data in model reports, and a lack of analysis regarding the clinical application value all contribute to the challenge of selecting the optimal model. Finally, while the average AUC of our best-developed model has achieved 0.87, some even exceeding 0.98, these findings may not fully translate into actual medical practice [ 62 ]. This is because although AUC is a widely utilized metric for evaluating the performance of predictive models, it only partially represents their actual efficacy. Pursuing excessively high values under the receiver operating characteristic AUC maybe over-optimization and potential distortion of the models [ 63 ].

Based on these methodological shortcomings, we make the following recommendations. First, models should be externally validated several times in different populations, and sample sizes must be adequately considered. Second, when data are missing, interpolation should be performed using multiple interpolation or machine learning. Third, predictive variables with incremental solid value should be mined based on clinical feasibility and applicability, and preventing overfitting should be emphasized in the predictive model. Fourth, it is clear that medical predictive modelling aims to construct a predictive tool with practical application value. Therefore, when constructing a medical predictive model, it is necessary to start from the practical point of view and give full consideration to the application prospect of the model. At the same time, we also need to recognize the importance of diversified evaluation, as far as possible, sensitivity, specificity, calibration index, net benefit, and DCA for comprehensive evaluation.

Conclusions

We identified 16 predictive models, most of the researchers reported excellent discrimination in their study. However, for various reasons, the risk of bias in nearly all models was high or unclear. Consequently, this finding implies that the predictive performance of these models might be overestimated, their accuracy in practical application to the target population remains questionable, and currently, we cannot endorse any of these predictive models for clinical practice. Additionally, our exploration of potential predictors, translating evidence into new insights for clinical practice. Future studies on predictive modelling of AD dementia risk in older adults should adhere to methodological guidelines and prioritize practicality and cost-effectiveness in model evaluation, thereby facilitating disease progression identification in older adults with MCI.

Data availability

All data generated or analyzed during this study are included in the Appendix. The corresponding author can provide the code upon request.

Abbreviations

• Alzheimer’s disease

Area under curve

Alzheimer’s disease assessment scale-cognitive subscale

Apolipoprotein E 4 allele

Highly phosphorylated tau protein

Activity of daily living

Clinical dementia rating

Decision curve analysis

• Mild cognitive impairment

Mini-Mental State Examination

Functional Activities Questionnaire

Random forests

Machine learning

Least absolute shrinkage and selection operator

Max-Relevance and Min-Redundancy

Risk of bias

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XW, PZ, and GC were instrumental in conceptualizing and designing the study. The protocol was formulated by XW and PZ, who also undertook the article screening and results reporting. YL, SZ, and NY offered analytical consultations and data verification. The initial draft of the manuscript was prepared by XW, with NY and HH providing critical reviews and revisions. All contributors sanctioned the final manuscript for publication and committed to being responsible for every aspect of the work.

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Wang, X., Zhou, S., Ye, N. et al. Predictive models of Alzheimer’s disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr 24 , 531 (2024). https://doi.org/10.1186/s12877-024-05044-8

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Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – original draft

Affiliation Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan

Roles Conceptualization, Project administration, Supervision, Writing – review & editing

* E-mail: [email protected]

Roles Data curation

Affiliation Department of Planning, Information and Management, University of Tokyo, Tokyo, Japan

Roles Supervision

•  [ ... ],

Affiliations Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan, International University of Health and Welfare, Tokyo, Japan

• [ view all ]
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• Hiroki Shinohara, 
• Satoshi Kodera, 
• Yugo Nagae, 
• Takashi Hiruma, 
• Atsushi Kobayashi, 
• Masataka Sato, 
• Shinnosuke Sawano, 
• Tatsuya Kamon, 
• Koichi Narita, 

• Published: June 18, 2024
• https://doi.org/10.1371/journal.pone.0304423
• Peer Review
• Reader Comments

Introduction

Ischemic heart disease is a leading cause of death worldwide, and its importance is increasing with the aging population. The aim of this study was to evaluate the accuracy of SurvTrace, a survival analysis model using the Transformer—a state-of-the-art deep learning method—for predicting recurrent cardiovascular events and stratifying high-risk patients. The model’s performance was compared to that of a conventional scoring system utilizing real-world data from cardiovascular patients.

This study consecutively enrolled patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Each patient’s initial PCI at our hospital was designated as the index procedure, and a composite of major adverse cardiovascular events (MACE) was monitored for up to two years post-index event. Data regarding patient background, clinical presentation, medical history, medications, and perioperative complications were collected to predict MACE. The performance of two models—a conventional scoring system proposed by Wilson et al. and the Transformer-based model SurvTrace—was evaluated using Harrell’s c-index, Kaplan–Meier curves, and log-rank tests.

A total of 3938 cases were included in the study, with 394 used as the test dataset and the remaining 3544 used for model training. SurvTrace exhibited a mean c-index of 0.72 (95% confidence intervals (CI): 0.69–0.76), which indicated higher prognostic accuracy compared with the conventional scoring system’s 0.64 (95% CI: 0.64–0.64). Moreover, SurvTrace demonstrated superior risk stratification ability, effectively distinguishing between the high-risk group and other risk categories in terms of event occurrence. In contrast, the conventional system only showed a significant difference between the low-risk and high-risk groups.

This study based on real-world cardiovascular patient data underscores the potential of the Transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients.

Citation: Shinohara H, Kodera S, Nagae Y, Hiruma T, Kobayashi A, Sato M, et al. (2024) The potential of the transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients with ischemic heart disease. PLoS ONE 19(6): e0304423. https://doi.org/10.1371/journal.pone.0304423

Editor: Marcelo Arruda Nakazone, Faculdade de Medicina de São José do Rio Preto, BRAZIL

Received: October 31, 2023; Accepted: May 12, 2024; Published: June 18, 2024

Copyright: © 2024 Shinohara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data contains personal information of each patient and therefore cannot be made public. Please contact the Department of Cardiology at the University of Tokyo Hospital ( [email protected] ) regarding data sharing. Data sharing is possible after obtaining individual permission from the Ethics Committee of the University of Tokyo Hospital.

Funding: Initials of the authors who received each award:H.S. Grant numbers awarded to each author:23K15152 The full name of each funder:Japan Society for the Promotion of Science URL of each funder website: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K15152/ Did the sponsors or funders play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript?: No.

Competing interests: The authors have declared that no competing interests exist.

Ischemic heart disease remains the leading cause of death worldwide, despite advancements in treatment modalities and therapeutic technologies [ 1 , 2 ]. As the population continues to age, improving the prognosis and treatment of ischemic heart disease has become increasingly important. Accurate patient risk stratification is crucial for optimizing treatment, and the effectiveness of scoring systems, such as the Suita score, has been well-documented [ 3 ]. Wilson et al. have also reported that scoring models incorporating age and history of catheterization are effective in predicting post-catheterization events [ 4 ].

In recent years, rapid advancements in machine learning have shown promise in surpassing conventional methods in patient risk assessment [ 5 , 6 ]. Beyond standard machine learning survival analysis, new deep learning survival models have been proposed [ 7 ]. Specifically, Wang et al. found that a deep learning model known as the “Transformer”, which employs an attention mechanism rather than recurrent neural networks or convolutional neural networks, is effective for survival time analysis [ 8 , 9 ]. The Transformer model has become pivotal in contemporary deep learning, serving as the foundation for systems like ChatGPT [ 10 , 11 ]. However, no studies have yet assessed the effectiveness of using the Transformer for survival analysis in the cardiovascular field. Therefore, the aim of this study was to compare and validate the accuracy of the novel Transformer-based model against conventional risk scoring model using real-world data from cardiovascular patients.

Study design and participants

This study involved consecutive enrollment of patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Within this timeframe, the initial PCI performed at our hospital was designated as the index procedure for each individual patient and used for analysis. Data were accessed and collected for research purposes from October 20, 2022 to December 28, 2022. Information that could identify individual participants was anonymized. A correspondence table was created to ensure that patient information could be accessed after collection, if necessary, while maintaining anonymity. The outcomes of these procedures were evaluated retrospectively. Data on patient background, clinical presentation, medical history, admission medications, perioperative complications, and discharge medications were extracted from the electronic health records (EHRs) of those who underwent the index PCI. Hypertension was defined as a systolic blood pressure of 140 mmHg or higher upon admission, a diastolic blood pressure of 90 mmHg or higher upon admission, or ongoing treatment with antihypertensive medications. Diabetes mellitus was defined by a hemoglobin A1c level ≥6.5% upon admission or ongoing treatment with either insulin or oral hypoglycemic agents. Dyslipidemia was defined as a low-density lipoprotein cholesterol level ≥140 mg/dL upon admission, a high-density lipoprotein cholesterol < 40 mg/dL upon admission, triglycerides ≥150 mg/dL upon admission, or ongoing use of dyslipidemia medications. Chronic kidney disease was defined as patients with an eGFR <60 mL/minute/1.73 m 2 , calculated using the Modification of Diet in Renal Disease (MDRD) equation [ 12 ] and serum creatinine levels upon admission modified by Japanese coefficients.

Missing data constituted 1.0% of all variables in the total dataset. These missing values were addressed using the multiple imputation method [ 13 ]. This technique substituted missing data points with a set of plausible alternatives, thereby generating multiple complete datasets for analysis. Each dataset was individually analyzed, and the results were then aggregated to produce a single, comprehensive result. In this study, we used Python to generate five pseudo-complete datasets, applying multiple imputations using the Bayesian Ridge method ( S1 File ).

To improve model interpretability and minimize multicollinearity, Pearson’s correlation coefficient was used to assess the correlation among explanatory variables. Any variable exhibiting a Pearson’s correlation coefficient exceeding 0.90 was omitted from the set of explanatory variables used for model training [ 14 ]. In cases where two features were highly correlated, the one with the greater overall correlation to all features was eliminated [ 14 ]. During the preprocessing phase, all continuous variables were standardized to have a mean value of 0 and a standard deviation of 1.

The endpoint consisted of a composite of major adverse cardiovascular events (MACE), including cardiac death, acute coronary syndrome, cerebrovascular event, and hospitalization for heart failure [ 4 ]. EHRs were used to collect data on these outcomes, as well as the period until their occurrence, for up to two years following the index procedure. Cardiac death was defined as death from acute myocardial infarction, ventricular arrhythmia, or heart failure [ 15 ]. Acute coronary syndrome was defined as nonfatal myocardial infarction or unstable angina [ 15 ]. Nonfatal myocardial infarction was defined as persistent angina accompanied by new ECG abnormalities and elevated cardiac biomarkers [ 15 ]. Unstable angina pectoris was defined as an extended episode of resting ischemic symptoms (typically exceeding 10 minutes) or a lowering of the activity threshold that induced accelerated chest pain, necessitating an unscheduled medical visit and an overnight stay—usually within 24 hours of the most recent symptoms—while not fulfilling myocardial infarction cardiac biomarker criteria [ 16 ]. Cerebrovascular events were defined as either cerebral hemorrhage or cerebral infarction. Survival time analyses were conducted on these outcomes until the respective dates of event onset. To compare the prognostic accuracy of the novel Transformer-based model with that of the conventional risk scoring model, the c-index was employed [ 17 ]. Subsequently, the risk stratification capabilities of each model were assessed by computing risk scores for every patient using the trained models. Patients in the test set were classified into high-, intermediate-, and low-risk score groups [ 18 ] and evaluated through Kaplan–Meier survival curves [ 19 ] and log-rank tests [ 20 ].

The impact of explanatory variables on outcomes was assessed using Shapley additive explanations (SHAP) [ 21 ]. An algorithmic evaluation method rooted in game theory, SHAP uses Shapley scores to estimate the contribution of each explanatory variable to the model’s prediction.

To assess the robustness of our findings, we performed three distinct sensitivity analyses: first, by omitting missing values; second, by adjusting the percentage of test sets; and third, by excluding patients with a history of PCI. This study was conducted in accordance with the revised Declaration of Helsinki and received approval from the institutional review board of the University of Tokyo Hospital (2021238NI-(2)). Informed consent was obtained in the form of an opt-out on a website.

To evaluate the predictive accuracy of MACE, we utilized the scoring system proposed by Wilson et al. [ 4 ] and SurvTrace, which is based on a model that uses a Transformer architecture [ 8 ].

The scoring system proposed by Wilson et al. serves as a predictive model for recurrent cardiovascular disease and incorporates variables such as age, smoking history, history of diabetes or heart failure, body mass index, number of diseased vessels, and history of statin or aspirin therapy. For the purposes of this study, it was defined as a conventional scoring model. SurvTrace is an alternative survival time analysis model that employs a Transformer, a specific deep learning technique. Using an attention mechanism, this model enables efficient calculation of the effect of each variable on survival time. All computational models were implemented using Python and executed on an Nvidia Tesla A-100 80GB graphics processing unit.

For data partitioning, 90% of the total dataset was randomly selected to constitute the training set. Subsequently, 25% of this training set was randomly allocated for validation during the model training process. The remaining 10% of the data, which was not included in the training set, served as a test set for assessing the accuracy of the trained models. Throughout the training process, Optuna, an advanced framework for hyperparameter optimization tailored for machine learning, was employed to fine-tune the model’s hyperparameters [ 22 ]. S2 File shows the SurvTrace execution code used.

Statistical analysis

Five pseudo-complete datasets were generated through the application of multiple imputation techniques to address missing values. The model’s accuracy was then calculated based on these five datasets. To synthesize the findings, the five accuracy estimates derived from each model were integrated using Rubin’s rules, facilitating a comparison of model performance [ 23 ].

For continuous variables, measurements were expressed as either mean (± standard deviation) or median (first and third quartiles), while categorical variables were reported as counts and frequencies (%).

The models’ prognostic accuracy was assessed using Harrell’s c-index [ 18 ]. Additionally, the risk stratification capabilities of each model were assessed through Kaplan–Meier curves [ 20 ] and log-rank tests [ 21 ]. The p value threshold for significance was set at <0.05. All statistical analyses were performed using Python 3.7.

Between January 1, 2005, and December 31, 2019, a total of 3938 first-time PCIs were performed in our hospital. Of these, 394 were designated as the test dataset, while the remaining 3544 cases were used for model training ( Fig 1 ). Among the patient information data collected from the EHRs at the University of Tokyo Hospital, 171 explanatory variables were used. Table 1 outlines the baseline characteristics of the key explanatory variables. The training dataset contained a significantly higher number of patients with a history of previous PCI compared with the test dataset. During the observation period, 683 subjects (17.3%) were lost to follow-up, including 610 cases in the training dataset and 73 cases in the test dataset.

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https://doi.org/10.1371/journal.pone.0304423.g001

https://doi.org/10.1371/journal.pone.0304423.t001

The c-index of SurvTrace outperformed that of the conventional scoring system, registering a mean c-index of 0.72 (95% confidence interval: 0.69–0.76), as opposed to a mean c-index of 0.64 (95% confidence interval: 0.64–0.64) for the conventional scoring system ( Table 2 , Fig 2 ). Fig 3 illustrates the learning curve of SurvTrace during its training process. The most accurate training model from among all trained risk prediction models, along with its dataset, was used to evaluate risk stratification capabilities. While the conventional scoring system showed that the low-risk group experienced significantly fewer events compared with the high-risk group, it did not show a significant difference between the intermediate-risk group and the other patient groups ( Fig 4 ). In contrast, SurvTrace revealed that the high-risk group had a significantly higher number of events than the other groups ( Fig 4 ).

This figure shows the c-index for both the conventional scoring system and SurvTrace. The upper and lower black lines represent the upper and lower limits of the 95% confidence intervals, respectively. The orange line shows the mean c-index value calculated from five pseudo-complete datasets.

https://doi.org/10.1371/journal.pone.0304423.g002

This figure illustrates the variation in the loss function over the course of the training process. The left panel shows the fluctuations in loss values for the training dataset, while the right panel shows these changes for the validation dataset.

https://doi.org/10.1371/journal.pone.0304423.g003

This figure shows the Kaplan–Meier curves generated by both the conventional scoring model and SurvTrace. The blue lines represent the Kaplan–Meier curve for the low-risk group as stratified by risk scores from both models. Similarly, the orange and green lines represent the curves for the intermediate- and high-risk groups, respectively. The translucent segments of each line indicate the 95% confidence interval.

https://doi.org/10.1371/journal.pone.0304423.g004

https://doi.org/10.1371/journal.pone.0304423.t002

Fig 5 presents the SHAP result, indicating that SurvTrace highlighted the influence of pre-existing conditions, such as a history of chronic heart failure.

This figure illustrates the Shapley additive explanations (SHAP) of SurvTrace. The horizontal axis indicates the impact on the model’s prediction, with points situated to the right representing a higher risk of future major adverse cardiovascular events (MACE) compared with points on the left. The vertical axis indicates the importance of the explanatory variables. In this model, a history of hospitalization for heart failure (HF) exerts the greatest impact on predicting the risk of future MACE events. The color of each dot indicates the high or low status within each variable; for example, in the “History of HF Hospitalization” column, red indicates that the patient has a history of HF hospitalization, while blue indicates no such history.

https://doi.org/10.1371/journal.pone.0304423.g005

In the first sensitivity analysis, cases with missing values were excluded from both training and test datasets. Post-exclusion, the training dataset comprised 2137 cases, and the test dataset contained 254 cases. The c-index for SurvTrace was 0.71, compared with 0.66 for the conventional scoring system. The second sensitivity analysis involved adjusting the proportion of the test dataset to 20%. Following this modification, the analysis was performed using one of the five pseudo-complete datasets generated by the multiple imputation method, including both training and test datasets. This adjustment yielded a c-index of 0.68 for SurvTrace and 0.66 for the conventional scoring system. In the final sensitivity analysis, after excluding patients with a history of PCI from one of the five pseudo-complete training and test datasets, the c-index for SurvTrace was 0.69, compared with 0.63 for the conventional scoring system.

This figure illustrates the flowchart of the study. Initially, all data were split into training and test datasets at a 9:1 ratio. To address missing values, multiple imputation was applied to both datasets, generating five pseudo-complete datasets for each. A separate 25% segment of the training dataset was reserved for validation. Subsequently, survival analysis was performed on each pseudo-complete dataset, and the c-index was calculated. Finally, Rubin’s rules were used to integrate the c-index values from each dataset to compute the final result. In the figure, yellow-green represents the data used for training the model, orange represents the validation data, and pink represents the data used for testing post-training.

This study demonstrated that SurvTrace, a predictive model using the Transformer deep learning algorithm, was effective in predicting recurrent cardiovascular events in patients with ischemic heart disease based on real-world clinical data. Compared with conventional scoring system, SurvTrace not only demonstrated superior accuracy in event prediction but also showed an improved ability to stratify high-risk patients.

The Transformer-based SurvTrace model demonstrated significantly higher prediction accuracy for recurrent cardiovascular events in patients with ischemic heart disease, using real-world clinical data, than did conventional scoring system. SurvTrace also demonstrated a significantly greater capacity for high-risk patient stratification relative to conventional scoring system. The model maintained its superior performance across a range of sensitivity analyses, which included the exclusion of missing values from the training and test datasets, modification of the test set percentages, and the exclusion of patients with a history of PCI. These results are consistent with previous studies that have underscored the superiority of machine learning and deep learning algorithms over conventional scoring systems [ 6 , 18 ]. The high accuracy of these advanced models is likely attributed to their ability to identify complex patterns among explanatory variables, a feature not present in conventional methods. Typically, conventional scoring systems rely on linear models, selecting only statistically significant explanatory variables. Such models necessitate explicit definitions of relationships between explanatory variables to account for any interactions, thereby increasing model complexity and raising concerns about multicollinearity and overfitting as the number of variables grows. In contrast, the Transformer algorithm can directly incorporate multiple explanatory variables into its models, capturing nonlinear relationships and complex interactions among them without the need for explicit definitions. In this study, while the conventional scoring system incorporated only important variables such as age, gender, and medical history, SurvTrace used all 171 explanatory variables. This comprehensive approach to feature inclusion may contribute to its higher predictive accuracy.

The Transformer model’s ability to stratify high-risk group more accurately than conventional scoring system has important implications for managing patients with ischemic heart disease in real-world clinical practice. Moreover, the alignment of our SHAP results with prior findings further underscores the robustness and validity of our study’s outcomes. The enhanced risk stratification capabilities of the Transformer model could potentially improve clinical decision making and assist physicians in tailoring treatment plans for individual patients [ 24 ]. Recent advancements have introduced large language models capable of automatically extracting structured data from electronic medical records [ 25 , 26 ]. Using these language models enables automated survival time analysis and future risk stratification based on individual patient records, offering a more personalized treatment approach that may potentially enhance intervention effectiveness and improve patient outcomes.

This study has several limitations that warrant consideration. First, our research relied on a dataset from a single institution, making it susceptible to potential selection bias. Future studies should address institution-specific biases by expanding and validating the diversity of the patient population through multicenter studies. Second, the sample size was relatively modest, comprising 3938 patients. In general, deep learning models require larger datasets to achieve high levels of accuracy; therefore, our sample size may have been insufficient. Third, although this study demonstrated the superiority of the Transformer model over conventional scoring system, it should be noted that the model used was specific to this study. Other Transformer models not evaluated in this study may yield different results. Fourth, this study was retrospective in nature, with events meticulously tracked in the EHRs. Despite this thorough tracking, some events might have been overlooked as a result of patients relocating or transferring to other hospitals, potentially leading to selection bias. To mitigate this issue, future prospective studies employing survival analysis with the Transformer model are necessary. Lastly, missing values in the dataset were handled using multiple imputation methods to facilitate the Transformer model’s application. These imputed values could introduce bias, especially for the Transformer model, as deep learning models are known to be sensitive to data noise.

This study demonstrated that a survival analysis model using Transformer, a state-of-the-art deep learning method, was significantly more accurate than the conventional scoring system in predicting recurrent cardiovascular events and stratifying high-risk patients using real-world clinical data. Additional research is warranted to further optimize the performance of deep learning models for more effective risk stratification and management of patients with ischemic heart disease.

Supporting information

S1 file. multiple imputation method execution file..

This file contains the code to execute the multiple imputation method in Python.

https://doi.org/10.1371/journal.pone.0304423.s001

S2 File. SurvTrace execution file.

This file contains the code to execute SurvTrace in Python.

https://doi.org/10.1371/journal.pone.0304423.s002

Acknowledgments

We thank Phoebe Chi, MD, from Edanz ( https://jp.edanz.com/ac ) for editing a draft of this manuscript.

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Introducing Apple Intelligence, the personal intelligence system that puts powerful generative models at the core of iPhone, iPad, and Mac

New Capabilities for Understanding and Creating Language

Image Playground Makes Communication and Self‑Expression Even More Fun

Genmoji Creation to Fit Any Moment

New Features in Photos Give Users More Control

Siri Enters a New Era

A New Standard for Privacy in AI

ChatGPT Gets Integrated Across Apple Platforms

Text of this article

June 10, 2024

PRESS RELEASE

Setting a new standard for privacy in AI, Apple Intelligence understands personal context to deliver intelligence that is helpful and relevant

CUPERTINO, CALIFORNIA Apple today introduced Apple Intelligence , the personal intelligence system for iPhone, iPad, and Mac that combines the power of generative models with personal context to deliver intelligence that’s incredibly useful and relevant. Apple Intelligence is deeply integrated into iOS 18, iPadOS 18, and macOS Sequoia. It harnesses the power of Apple silicon to understand and create language and images, take action across apps, and draw from personal context to simplify and accelerate everyday tasks. With Private Cloud Compute, Apple sets a new standard for privacy in AI, with the ability to flex and scale computational capacity between on-device processing and larger, server-based models that run on dedicated Apple silicon servers.

“We’re thrilled to introduce a new chapter in Apple innovation. Apple Intelligence will transform what users can do with our products — and what our products can do for our users,” said Tim Cook, Apple’s CEO. “Our unique approach combines generative AI with a user’s personal context to deliver truly helpful intelligence. And it can access that information in a completely private and secure way to help users do the things that matter most to them. This is AI as only Apple can deliver it, and we can’t wait for users to experience what it can do.”

Apple Intelligence unlocks new ways for users to enhance their writing and communicate more effectively. With brand-new systemwide Writing Tools built into iOS 18, iPadOS 18, and macOS Sequoia, users can rewrite, proofread, and summarize text nearly everywhere they write, including Mail, Notes, Pages, and third-party apps.

Whether tidying up class notes, ensuring a blog post reads just right, or making sure an email is perfectly crafted, Writing Tools help users feel more confident in their writing. With Rewrite, Apple Intelligence allows users to choose from different versions of what they have written, adjusting the tone to suit the audience and task at hand. From finessing a cover letter, to adding humor and creativity to a party invitation, Rewrite helps deliver the right words to meet the occasion. Proofread checks grammar, word choice, and sentence structure while also suggesting edits — along with explanations of the edits — that users can review or quickly accept. With Summarize, users can select text and have it recapped in the form of a digestible paragraph, bulleted key points, a table, or a list.

In Mail, staying on top of emails has never been easier. With Priority Messages, a new section at the top of the inbox shows the most urgent emails, like a same-day dinner invitation or boarding pass. Across a user’s inbox, instead of previewing the first few lines of each email, they can see summaries without needing to open a message. For long threads, users can view pertinent details with just a tap. Smart Reply provides suggestions for a quick response, and will identify questions in an email to ensure everything is answered.

Deep understanding of language also extends to Notifications. Priority Notifications appear at the top of the stack to surface what’s most important, and summaries help users scan long or stacked notifications to show key details right on the Lock Screen, such as when a group chat is particularly active. And to help users stay present in what they’re doing, Reduce Interruptions is a new Focus that surfaces only the notifications that might need immediate attention, like a text about an early pickup from daycare.

In the Notes and Phone apps, users can now record, transcribe, and summarize audio. When a recording is initiated while on a call, participants are automatically notified, and once the call ends, Apple Intelligence generates a summary to help recall key points.

Apple Intelligence powers exciting image creation capabilities to help users communicate and express themselves in new ways. With Image Playground, users can create fun images in seconds, choosing from three styles: Animation, Illustration, or Sketch. Image Playground is easy to use and built right into apps including Messages. It’s also available in a dedicated app, perfect for experimenting with different concepts and styles. All images are created on device, giving users the freedom to experiment with as many images as they want.

With Image Playground, users can choose from a range of concepts from categories like themes, costumes, accessories, and places; type a description to define an image; choose someone from their personal photo library to include in their image; and pick their favorite style.

With the Image Playground experience in Messages, users can quickly create fun images for their friends, and even see personalized suggested concepts related to their conversations. For example, if a user is messaging a group about going hiking, they’ll see suggested concepts related to their friends, their destination, and their activity, making image creation even faster and more relevant.

In Notes, users can access Image Playground through the new Image Wand in the Apple Pencil tool palette, making notes more visually engaging. Rough sketches can be turned into delightful images, and users can even select empty space to create an image using context from the surrounding area. Image Playground is also available in apps like Keynote, Freeform, and Pages, as well as in third-party apps that adopt the new Image Playground API.

Taking emoji to an entirely new level, users can create an original Genmoji to express themselves. By simply typing a description, their Genmoji appears, along with additional options. Users can even create Genmoji of friends and family based on their photos. Just like emoji, Genmoji can be added inline to messages, or shared as a sticker or reaction in a Tapback.

Searching for photos and videos becomes even more convenient with Apple Intelligence. Natural language can be used to search for specific photos, such as “Maya skateboarding in a tie-dye shirt,” or “Katie with stickers on her face.” Search in videos also becomes more powerful with the ability to find specific moments in clips so users can go right to the relevant segment. Additionally, the new Clean Up tool can identify and remove distracting objects in the background of a photo — without accidentally altering the subject.

With Memories, users can create the story they want to see by simply typing a description. Using language and image understanding, Apple Intelligence will pick out the best photos and videos based on the description, craft a storyline with chapters based on themes identified from the photos, and arrange them into a movie with its own narrative arc. Users will even get song suggestions to match their memory from Apple Music. As with all Apple Intelligence features, user photos and videos are kept private on device and are not shared with Apple or anyone else.

Powered by Apple Intelligence, Siri becomes more deeply integrated into the system experience. With richer language-understanding capabilities, Siri is more natural, more contextually relevant, and more personal, with the ability to simplify and accelerate everyday tasks. It can follow along if users stumble over words and maintain context from one request to the next. Additionally, users can type to Siri, and switch between text and voice to communicate with Siri in whatever way feels right for the moment. Siri also has a brand-new design with an elegant glowing light that wraps around the edge of the screen when Siri is active.

Siri can now give users device support everywhere they go, and answer thousands of questions about how to do something on iPhone, iPad, and Mac. Users can learn everything from how to schedule an email in the Mail app, to how to switch from Light to Dark Mode.

With onscreen awareness, Siri will be able to understand and take action with users’ content in more apps over time. For example, if a friend texts a user their new address in Messages, the receiver can say, “Add this address to his contact card.”

With Apple Intelligence, Siri will be able to take hundreds of new actions in and across Apple and third-party apps. For example, a user could say, “Bring up that article about cicadas from my Reading List,” or “Send the photos from the barbecue on Saturday to Malia,” and Siri will take care of it.

Siri will be able to deliver intelligence that’s tailored to the user and their on-device information. For example, a user can say, “Play that podcast that Jamie recommended,” and Siri will locate and play the episode, without the user having to remember whether it was mentioned in a text or an email. Or they could ask, “When is Mom’s flight landing?” and Siri will find the flight details and cross-reference them with real-time flight tracking to give an arrival time.

To be truly helpful, Apple Intelligence relies on understanding deep personal context while also protecting user privacy. A cornerstone of Apple Intelligence is on-device processing, and many of the models that power it run entirely on device. To run more complex requests that require more processing power, Private Cloud Compute extends the privacy and security of Apple devices into the cloud to unlock even more intelligence.

With Private Cloud Compute, Apple Intelligence can flex and scale its computational capacity and draw on larger, server-based models for more complex requests. These models run on servers powered by Apple silicon, providing a foundation that allows Apple to ensure that data is never retained or exposed.

Independent experts can inspect the code that runs on Apple silicon servers to verify privacy, and Private Cloud Compute cryptographically ensures that iPhone, iPad, and Mac do not talk to a server unless its software has been publicly logged for inspection. Apple Intelligence with Private Cloud Compute sets a new standard for privacy in AI, unlocking intelligence users can trust.

Apple is integrating ChatGPT access into experiences within iOS 18, iPadOS 18, and macOS Sequoia, allowing users to access its expertise — as well as its image- and document-understanding capabilities — without needing to jump between tools.

Siri can tap into ChatGPT’s expertise when helpful. Users are asked before any questions are sent to ChatGPT, along with any documents or photos, and Siri then presents the answer directly.

Additionally, ChatGPT will be available in Apple’s systemwide Writing Tools, which help users generate content for anything they are writing about. With Compose, users can also access ChatGPT image tools to generate images in a wide variety of styles to complement what they are writing.

Privacy protections are built in for users who access ChatGPT — their IP addresses are obscured, and OpenAI won’t store requests. ChatGPT’s data-use policies apply for users who choose to connect their account.

ChatGPT will come to iOS 18, iPadOS 18, and macOS Sequoia later this year, powered by GPT-4o. Users can access it for free without creating an account, and ChatGPT subscribers can connect their accounts and access paid features right from these experiences.

Availability

Apple Intelligence is free for users, and will be available in beta as part of iOS 18 , iPadOS 18 , and macOS Sequoia  this fall in U.S. English. Some features, software platforms, and additional languages will come over the course of the next year. Apple Intelligence will be available on iPhone 15 Pro, iPhone 15 Pro Max, and iPad and Mac with M1 and later, with Siri and device language set to U.S. English. For more information, visit apple.com/apple-intelligence .

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Business Risks Diagram PowerPoint Template

The Business Risks Diagram PowerPoint Template is an editable presentation slide for business risks. Business risks are the estimated pitfalls that may impart retarding effects on business success. This retardation can happen both from financial and goal achievement perspectives. So, professionals make some crucial considerations initially to avoid loss in future business processes. This PPT template provides a modern diagram to showcase the categories and examples of all the risks. The diagram template is decorated through a decent color scheme and meaningful icons for enhanced communication with the teams or stakeholders. Professionals can use this slide to discuss the effects of mitigation strategies in multiple situations. 

The Business Risks Diagram PowerPoint Template shows a semi-circle bridge diagram divided into seven segments. A dual gradient color is used in this diagram. Through differential darkening at the inner edge of the diagram, a 3D effect is created on the bridge structure. The hollow core unit is to mention the presentation title, business name, or any other subject. On the first slide, these segments mention the categories of risks, e.g., production, price, casualty, technology, relationship, regulatory, and human. Through thin straight lines, these segments are associated with the outer text boxes that mention the risks along with the meaningful icons. For instance, the production risks are weather, health, field loss, and spoilage, and the factory icon represents this category. The following slide differs because it shows the infographic PowerPoint icons in the inner segments of the diagram. In place of the icons around the outer side, there are risk category titles and risk names. 

Presenters can conveniently alter the text and icons according to presentation needs. These slides are for business risk presentations; however, health professionals, marketing teams, and other professionals can also modify the text for risk presentations. So, our Business Risks Diagram PowerPoint Template can be used for meetings and online presentations because of its compatibility with all PowerPoint versions.

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