Deep Learning for Cardiovascular Risk Stratification
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11936_2020_Article_814.pdf
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1.36 MB
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Adobe PDF
Checksum (MD5)
1f9f1ae71bcb8aa1239a014dd955d1e0
Author(s)
Schlesinger, Daphne E.
Stultz, Collin M
Date Issued
June 2020
Journal
Current Treatment Options in Cardiovascular Medicine
Publisher
Springer Science and Business Media LLC
Citation
Schlesinger, Daphne E. and C.M. Stultz. "Deep Learning for Cardiovascular Risk Stratification." Current Treatment Options in Cardiovascular Medicine 22, 8 (June 2020): 15 © 2020 Springer Nature
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11936-020-00814-0
