Personalized treatment for coronary artery disease patients: a machine learning approach
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Personalized treatment for coronary artery disease patients: a machine learning approach Dimitris Bertsimas1 · Agni Orfanoudaki2
· Rory B. Weiner3
Received: 18 March 2020 / Accepted: 17 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients’ medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R 2 = 0.801 the outcome of interest; the time from diagnosis to a potential adverse event (TAE). Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, the goal of ML4CAD is to identify for every patient the therapy with the best expected TAE using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool. Keywords Precision medicine · Personalization · Coronary artery disease · Machine learning · Prescriptions Highlights •
We present the first prescriptive methodology that utilizes electronic medical records and machine learning
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10729-020-09522-4) contains supplementary material, which is available to authorized users. Agni Orfanoudaki
[email protected] Dimitris Bertsimas [email protected] Rory B. Weiner [email protected] 1
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
2
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
3
Cardiology Division, Massachusetts General Hospital, Boston, MA 02114, USA
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to provide personalized treatment recommendations for the management of coronary artery disease patients. We introduce a new quantitative framework to evaluate the performance of prescriptive algorithms. We show that our data-driven approach can substantial
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