# Leveraging deep learning and clinical notes for surveillance and prediction of intentional self-harm and suicide

> **NIH NIH R56** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2021 · $551,840

## Abstract

PROJECT SUMMARY:
Suicide is one of the leading causes of death in the United States, with more than 47,000
individuals dying by suicide each year. The identification of individuals at risk for suicide is an
important step for a comprehensive approach to suicide prevention. Despite extensive research
on risk factors for intentional self-harm and suicide, prospective prediction of suicide remains a
difficult task with poor predictive power. Recent studies suggest that new machine learning
methods applied to electronic health records (EHR) show promising results. However, more
advanced computational approaches such as deep learning, have not been fully leveraged in
this field, especially in the area of advanced methods for text classification of clinical notes. Our
aims in this project, are to improve the phenotyping of suicidal behavior, and the prediction of
future suicidal behavior and suicide deaths by integrating mortality data with EHR data and
leveraging state-of-the-art natural language computational approaches. We will also investigate
methods for explain ability and interpretability of the models to improve future adoption by
clinicians. We will validate our models by examining reproducibility and generalizability across
two health systems using similar data at both sites.

## Key facts

- **NIH application ID:** 10330113
- **Project number:** 1R56MH124744-01
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Jihad S Obeid
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $551,840
- **Award type:** 1
- **Project period:** 2021-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330113

## Citation

> US National Institutes of Health, RePORTER application 10330113, Leveraging deep learning and clinical notes for surveillance and prediction of intentional self-harm and suicide (1R56MH124744-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10330113. Licensed CC0.

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