# Deep probabilistic predictive models for stroke and coronary heart disease

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2022 · $633,013

## Abstract

Project Summary
 Cardiovascular disease negatively affects millions of people worldwide. Globally, it accounts for approximately
thirty percent of all deaths. Furthermore, a significant fraction of deaths caused by cardiovascular disease occur
in a non-geriatric population; fifteen percent of all worldwide deaths are attributed to cardiovascular disease
for people under the age of seventy. Treatment to prevent cardiovascular events should be based on highly
individualized risk prediction. High risk patients should get more aggressive treatments because the risk of
disease outweighs the burden of treatment, while low risk patients should be managed more conservatively.
For example, anti-thrombotic therapy for coronary heart disease may increase bleeding risk and may not be
appropriate for low-risk patients. Two primary kinds of cardiovascular disease are stroke and coronary heart
disease, and there have been a number of developments in risk scores for both ailments. However, these risk
scores only use a small fraction of the available measurements about a patient and treat risk as a collection of
independent factors rather than considering how their interactions amplify or ameliorate risk. Moreover, a majority
of the popular coronary heart disease and stroke risk scores are designed to be manually computed by a busy
physician at the point of care, which further limits their scope and fidelity. Next generation risk scores for stroke
and cardiovascular disease should take into account all of the available information in the electronic health record
without the constraints of the parametric assumptions of traditional risk modeling. More accurate risk assessment
of coronary heart disease and stroke will lead to better care and reduce the cardiovascular disease burden.
 Our vision is to capitalize on large collections of electronic health records along with recent advances in
deep learning to build risk scores that use more available health information while making minimal mathematical
assumptions about the nature of clinical risk. Our proposal propels the field from human computable independent
risks calculations necessitated by previous limitations of technology to calculations that make use of deep learning
to learn highly nonlinear risks and risk factor interactions. We additionally demonstrate how deep learning can be
used to deal with the ever-present issue of missing values in medicine. Our proposal also targets an area under-
explored by previous work on risk scores: fairness. Treatment quality is affected by the quality of risk estimation.
This means populations where estimated risk is less accurate may receive worse care. Risk scores developed
with simple models may only capture risk accurately for the majority population as simple models are not flexible
enough to cover multiple populations. We seek to identify potential risk calculation differences with respect to
race and ethnicity. We will construct and evaluate deep learning methods for ...

## Key facts

- **NIH application ID:** 10439509
- **Project number:** 5R01HL148248-04
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Rajesh Ranganath
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $633,013
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439509

## Citation

> US National Institutes of Health, RePORTER application 10439509, Deep probabilistic predictive models for stroke and coronary heart disease (5R01HL148248-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10439509. Licensed CC0.

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