# Can suicide theory-guided natural language processing of clinical progress notes improve existing prediction models of Veteran suicide mortality?

> **NIH VA I01** · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · 2024 · —

## Abstract

Background: Reducing suicide and suicide attempts among U.S. Veterans is a major national priority, as
more than 6,000 Veterans die by suicide every year and many more attempt suicide. In 2017, the most recent
year for which data are available, the suicide rate among Veterans was 1.5 times the rate of non-Veterans, and
the suicide rate among female Veterans was 2.2 times the rate of non-Veteran females. Current VHA suicide
risk prediction models suffer from high numbers of false negatives - Veterans not deemed at high risk of
suicide who do attempt or die by suicide. These suicide prediction models have not incorporated the rich
information from clinical progress notes that may improve our ability to predict suicidal behavior. Much of this
information in clinical progress notes is unstructured free text. A suicide-specific ontology and information
extraction system that can extract suicide-related information from unstructured clinical progress notes is not
available.
Significance/Impact: Enhancing VHA's ability to identify Veterans who are most likely to attempt suicide
ensures that limited intervention resources can be focused on Veterans with the highest risk, before they
attempt suicide or die by suicide. The proposed study is well-aligned with priorities for HSR&D research and
with VA strategic goals for 2018 – 2024 set out by VA leadership, who listed suicide prevention as “VA's
highest clinical priority.”
Innovation: Our key methodological innovation is to pair a state-of-the-art theoretical framework (3-step
Theory of Suicide) to predict who is most likely to act on their suicidal thoughts with state-of-the-art data
science methods (NLP, machine learning). Since our suicide-theory concepts, that is hopelessness,
connectedness, psychological pain, and capacity for suicide, are not represented in structured patient data, we
will develop novel NLP and information extraction tools and apply them to clinical progress notes, the potential
of which has not been fully levied to improve suicide prediction models.
Specific Aims: We have three specific aims:
 1. Develop a suicide-specific ontology for machine recognition of hopelessness, connectedness,
 psychological pain, and capacity for suicide in progress notes of clinical encounters with Veterans who
 attempted or died by suicide.
 2. Extract information on the presence and intensity of hopelessness, connectedness, psychological pain,
 and capacity for suicide in clinical progress notes and describe change in these concepts in proximity of
 a suicide or suicide attempt.
 3. Determine the predictive validity of hopelessness, connectedness, psychological pain, and capacity for
 suicide regarding Veteran suicide attempts and mortality in two prediction models that VA currently
 uses in clinical practice: STORM and REACHVET.
Methodology: The proposed mixed-methods study has an exploratory sequential design where a qualitative
component (Aim 1) informs quantitative analyses (Aims 2 and 3). Data coll...

## Key facts

- **NIH application ID:** 10991655
- **Project number:** 5I01HX003122-03
- **Recipient organization:** VETERANS ADMIN PALO ALTO HEALTH CARE SYS
- **Principal Investigator:** Alex Sox-Harris
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2021-06-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10991655

## Citation

> US National Institutes of Health, RePORTER application 10991655, Can suicide theory-guided natural language processing of clinical progress notes improve existing prediction models of Veteran suicide mortality? (5I01HX003122-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10991655. Licensed CC0.

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