Personalized treatment for coronary artery disease patients: a machine learning approach

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 R² = 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.

Appendix

Acronym	Acronym Definition
AHA	American Heart Association
ASA	Aspirin
AUC	Area Under the ROC Curve
BMC	Boston Medical Center
BMI	Body Mass Index
CABG	Coronary Artery Bypass Graft
CAD	Coronary Artery Disease
CART	Classification and Regression Trees
DMLA	Degree of ML Agreement
ECG	Electrocardiogram
EMR	Electronic Medical Records
FDA	US Food and Drug Administration
HDL	High-Density Lipoprotein
k-NN	k-Nearest Neighbors
LDL	Low-Density Lipoprotein
ML	Machine Learning
OCT	Optimal Classification Trees
ORT	Optimal Regression Trees
PE	Prescription Effectiveness
PR	Prescription Robustness
PCI	Percutaneous Coronary Intervention
ROC	Receiver Operator Characteristic
TAE	Time from diagnosis to a potential Adverse Event

List of all acronyms used in the manuscript in alphabetical order along with the corresponding definition.