Personalized treatment for coronary artery disease patients: a machine learning approach
Participer
INFORMATIONS SYSTEMS ET OPERATIONS MANAGEMENT
Intervenant : Agni Orfanoudaki
PhD Candidate in the Operations Research Center at the
Massachusetts Institute of Technology
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 createddata-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 squared = 0.801 the time from diagnosis to a potential adverse event (TAE) and gain accurate approximations of the counterfactual treatment effects. Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, ML4CAD identifies for every patient the therapy with the best expected outcome 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.
Pdf: https://arxiv.org/pdf/1910.08483.pdf
Bio :
"My research vision is to combine my experience and training in operations research with my creativity to build analytical models that make a positive impact on society. To pursue this vision, my research agenda currently focuses on developing new methods and models for healthcare practitioners using data-driven, machine learning and optimization techniques. Moreover, I am particularly interested in studying the implications of these models on automated decision making. For this reason, I aim to lay the foundations, both in terms of theory and applications, in the area of algorithmic insurance, a topic that I initiated during my doctoral studies."