Deep Learning for Cardiovascular Risk Stratification

• Main Authors: Schlesinger, Daphne E. (Author), Stultz, Collin M (Author)
• Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Springer Science and Business Media LLC, 2021-09-20T17:29:52Z.
• Subjects: Article
• Online Access: Get fulltext

 Purpose of review Although deep learning represents an exciting platform for the development of risk stratification models, it is challenging to evaluate these models beyond simple statistical measures of success, which do not always provide insight into a model's clinical utility. Here we propose a framework for evaluating deep learning models and discuss a number of interesting applications in light of these rubrics. Recent findings Data scientists and clinicians alike have applied a variety of deep learning techniques to both medical images and structured electronic medical record data. In many cases, these methods have resulted in risk stratification models that have improved discriminatory ability relative to more straightforward methods. Nevertheless, in many instances, it remains unclear how useful the resulting models are to practicing clinicians. Summary To be useful, deep learning models for cardiovascular risk stratification must not only be accurate but they must also provide insight into when they are likely to yield inaccurate results and be explainable in the sense that health care providers can understand why the model arrives at a particular result. These additional criteria help to ensure that the model can be faithfully applied to the demographic for which it is most accurate.