# Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding

> **NIH NIH K23** · YALE UNIVERSITY · 2023 · $193,860

## Abstract

PROJECT SUMMARY
 Acute gastrointestinal bleeding accounts for over 2.2 million hospital days and 19.2 billion dollars of
medical charges annually. 52% to 55% of patients with acute gastrointestinal bleeding are unnecessarily
hospitalized, leading to wasted resources. Although risk stratification of patients presenting with gastrointestinal
bleeding is recommended, risk-assessment scoring systems are not commonly used in practice, have sub-
optimal performance, may be applied incorrectly, and are not easily updated.
 Most current risk scores were designed for use based on the location of the bleeding source: upper or
lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. A
risk score that bases initial assessment on presenting symptoms (e.g., hematemesis, melena, hematochezia) is
more relevant and useful in clinical practice. The electronic health record can be used to identify patients with
acute gastrointestinal bleeding symptoms and extract clinical data to automatically calculate risk scores that are
made available to providers. Machine learning (field of study that gives computers the ability to learn without
being explicitly programmed), particularly deep learning using neural networks (collection of nodes that process
and transmit information), can create electronic health record-based models that perform better than clinical risk
scores for gastrointestinal bleeding and are well-suited for learning from new data.
 This proposal will use deep learning tools on electronic health record data to decrease unnecessary
hospitalization in patients with acute gastrointestinal bleeding by identifying low risk patients. The goals are to 1)
Develop and validate an accurate and clinically useful deep learning algorithm for initial risk stratification superior
to existing clinical risk scores 2) Develop and validate a dynamic deep learning tool to model risk over time, and
3) Pilot the best performing algorithms in the electronic health record. Deep learning algorithms will be developed
using a dataset of electronic health record data of 7,000 patients with acute gastrointestinal bleeding from two
academic hospitals in the Yale-New Haven Health System. Validation will be performed on a separate dataset
of patients at Partners Healthcare in Boston, Massachusetts. Neural network approaches will be applied to
patients’ data updated over time to evaluate the trajectory towards requiring transfusion of red blood cells. Finally,
a pilot study will implement the best-performing algorithms in the electronic health record for a 3-month period
to test feasibility of deployment and acceptability to providers and patients. Planned coursework includes deep
learning with biomedical data, risk assessment and longitudinal analysis.
 This work has potential to generate cost savings through better integrated risk stratification of patients
presenting with overt gastrointestinal bleeding. To meet the research and ed...

## Key facts

- **NIH application ID:** 10696199
- **Project number:** 5K23DK125718-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Dennis Shung
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $193,860
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10696199

## Citation

> US National Institutes of Health, RePORTER application 10696199, Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding (5K23DK125718-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10696199. Licensed CC0.

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