# Detecting bleeding events using EHRs for prediction in Afib

> **NIH NIH R01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2020 · $808,962

## Abstract

Abstract
Atrial fibrillation (AF) affects millions of mostly older Americans. Atrial fibrillation
contributes to stroke and weighing stroke risk against risk of bleeding from oral
anticoagulants (AC) is central to AF management. AC decision-making has become
more complex in recent years with the introduction of several target specific oral AC
agents. It remains challenging to advise older AF patients about AC since they are
frequently at high risk for stroke and complications from AC, particularly bleeding, and
data are limited on real-world outcomes of AC among vulnerable populations. Our
project, Detecting bleeding Events using Electronic records for Prediction in Atrial
Fibrillation (DEEP AF), will leverage novel biomedical natural language processing
(NLP) approaches to integrate bleeding-related information from both the structured and
unstructured EHR records and statistical and deep learning approaches to predict risk of
bleeding on AF patients on AC therapy. DEEP AF will address present knowledge gaps
to accurately identify bleeding events from longitudinal electronic health records and to
improve bleeding prediction accuracy and focus on modifiable, clinically relevant risk
factors, thereby facilitating future large-scale comparative effectiveness studies
evaluating AC agents, and helping clinicians develop better approaches to weighing AC
risks and benefits.

## Key facts

- **NIH application ID:** 9867745
- **Project number:** 5R01HL137794-03
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** David D. McManus
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $808,962
- **Award type:** 5
- **Project period:** 2018-04-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9867745

## Citation

> US National Institutes of Health, RePORTER application 9867745, Detecting bleeding events using EHRs for prediction in Afib (5R01HL137794-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9867745. Licensed CC0.

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