Developing Explainable AI for Equitable Risk Stratification of Atrial Fibrillation and Stroke

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PROJECT SUMMARY Atrial fibrillation (AF) leads to significant morbidity, mortality, and over $6B in annual hospitalization costs among the nearly 6 million US adults it affects. AF is a cardiac arrhythmia which can cause blood to collect in the atria and form clots that travel to the brain resulting in a stroke. Efforts to reduce rates of stroke related to AF are limited by rudimentary stroke risk stratification tools and disparities in care. There is a critical need for personalized, socially aware, equitable stroke risk prediction among patients with AF to enable optimal implementation of contemporary stroke-prevention therapies. The objective of this proposal is to use artificial intelligence (AI) and machine learning methods to capture and quantify synergies among known and newly discovered AF risk factors in socioeconomic contexts. My central hypothesis is that stroke prevention can be improved through methods that leverage computational methods on large datasets augmented with information on social determinants of health (SDoH). Preliminary studies by our group and others have revealed subgroups of patients for whom SDoH factors are critical for accurate risk stratification. Aim 1 is to discover new risk-factor relationships for patients with AF that include SDoH data, using an innovative comorbidity discovery framework (Poisson Binomial Comorbidity Discovery). Aim 2 focuses on building models that combine the variables identified in Aim 1 with established risk factors to predict outcomes using AI methods. To do so, I will build novel Probabilistic Graphical Models (PGMs) to understand the impact of SDoH and newly identified factors on AF-related stroke risk. The primary innovation in this proposal is employing novel analytic approaches to understand and reduce disparities in AF risk prediction models. The proposal aims to provide means for improved care across the spectrum of patients with AF and address disparities in the present standard of care. The AI tools created will be readily accessible and interpretable by clinicians and patients to help guide individual treatment decisions. Completion of this proposal will yield a personalized and equitable approach to stroke prevention in the context of AF. This project provides multidisciplinary computational and clinical training augmented with mentorship from experts in both domains. The outlined training will provide me with the computational and translational cardiology experiences required to succeed as an independent investigator and physician-scientist.