# Predicting Self-Harm, Suicide Attempt, and Suicidal Death using Longitudinal EHR, Claims and Mortality Data

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $749,141

## Abstract

PROJECT ABSTRACT
Suicide is one of the leading causes of death. As of 2015, annual age-adjusted suicide rate in the U.S. is 13.26
per 100,000 individuals, and on average, there are 121 suicides per day. While white males between 45 and 64
years of age are 4 times more likely than females to die by suicide, females attempt suicide 3 times as often as
males. Recent data suggest that there are 20 times as many suicide attempts, which is generally considered a
high and consistent risk factor for subsequent suicide. However, predicting and monitoring when someone will
attempt self-harm and suicide has been nearly impossible. In this project, we plan to leverage large-scale,
integrated electronic health record and claims from the New York City Clinical Data Research Network to study
the suicidality in relation to emergency department (ED) visits or hospitalizations. In particular, using data on >10
million patients, we will develop novel NLP and machine learning models to identify patients at highest risk for
self-harm, suicide attempt and suicide, and conduct a pilot study to assess the clinical utility of such models. We
will also conduct a validation study using similar data from Kaiser Permanente Washington.

## Key facts

- **NIH application ID:** 9964905
- **Project number:** 5R01MH119177-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** GEORGE S ALEXOPOULOS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $749,141
- **Award type:** 5
- **Project period:** 2019-06-24 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964905

## Citation

> US National Institutes of Health, RePORTER application 9964905, Predicting Self-Harm, Suicide Attempt, and Suicidal Death using Longitudinal EHR, Claims and Mortality Data (5R01MH119177-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9964905. Licensed CC0.

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