# Personalized Risk Stratification in Atrial Fibrillation using Portable, Explainable Artificial Intelligence

> **NIH NIH R56** · DENVER HEALTH AND HOSPITAL AUTHORITY · 2023 · $454,228

## Abstract

PROJECT SUMMARY/ABSTRACT
Implementation of contemporary strategies to reduce stroke related to atrial fibrillation (AF) is limited by (1)
rudimentary stroke risk stratification tools and (2) disparities in care and outcomes of AF. There remains a
critical need for personalized, socially-aware, equitable stroke risk prediction among patients with AF, in order
to optimally implement contemporary stroke-prevention therapies. A major long-term goal is to develop a
portable, equitable risk-stratification tool to improve stroke-prevention among patients with AF. The objectives
of this project are to (i) discover new risk-factor relationships for patients with AF that incorporate social
determinants of health (SDoH), using an innovative comorbidity discovery framework (Poisson Binomial
Comorbidity [PBC]); (ii) combine these with established risk factors using explainable, artificial-intelligence (AI)
methods; and (iii) develop, deploy and test an augmented, personalized stroke risk stratification tool for AF
patients across different health systems in a disparity-aware fashion. Our central hypothesis is that stroke
prevention can be improved through methods that: leverage all available data, including SDoH; capture and
quantify synergies among known and newly-discovered risk factors in socioeconomic context; and can be
ported to other health systems, adapting to different populations. The rationale for this project is that current
AF-related stroke risk management lacks the precision and awareness required to optimally implement
treatments because it does not adequately account for (1) population diversity, (2) SDoH and disparities, (3)
synergistic interactions among risk factors, and (4) novel, emerging risk factors. The central hypothesis will be
tested by pursuing three specific aims: 1) Discover new clinical and socioeconomic relationships that
determine stroke risk in patients with AF; 2) Develop a socially-conscious, AI-based machinery for calculating
personalized stroke risk among patients with AF; and 3) Benchmark an AI-based, socially-aware stroke risk
predictor across a diverse cohort of health systems using PCORnet and use it to discover biases and drivers of
downstream care disparities. In the first aim, the PBC approach will be used to leverage large datasets that
include SDoH, in order identify new risk markers. The second aim will focus on building novel, Probabilistic
Graphical Models (PGMs) to understand the impact of SDoH on AF-related stroke risk. In the third aim, the
models will be tested across a diverse set of healthcare systems to understand portability, diversity, and bias.
The research proposed in this application is innovative because it (1) leverages uniquely-available data on
SDoH, (2) employs a much more powerful and portable analytic approach to understand risk; and (3) is
designed with an eye towards understanding and reducing disparities and bias in risk prediction models. The
proposed research is significant because it ...

## Key facts

- **NIH application ID:** 11199840
- **Project number:** 7R56HL168264-02
- **Recipient organization:** DENVER HEALTH AND HOSPITAL AUTHORITY
- **Principal Investigator:** BENJAMIN ADAM STEINBERG
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $454,228
- **Award type:** 7
- **Project period:** 2023-09-05 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11199840

## Citation

> US National Institutes of Health, RePORTER application 11199840, Personalized Risk Stratification in Atrial Fibrillation using Portable, Explainable Artificial Intelligence (7R56HL168264-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11199840. Licensed CC0.

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