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

> **NIH NIH F30** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $45,820

## Abstract

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.

## Key facts

- **NIH application ID:** 10929399
- **Project number:** 5F30HL170689-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Raquel Reisinger
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $45,820
- **Award type:** 5
- **Project period:** 2023-09-01 → 2025-05-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929399

## Citation

> US National Institutes of Health, RePORTER application 10929399, Developing Explainable AI for Equitable Risk Stratification of Atrial Fibrillation and Stroke (5F30HL170689-02). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10929399. Licensed CC0.

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