# Improving Suicide Risk Prediction in Racial, Ethnic, and Linguistic Minority Youth

> **NIH NIH P50** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $248,692

## Abstract

The proportion of pediatric emergency department or inpatient visits due to suicide-related behaviors (SRB)
has doubled in recent years. This public health emergency appears to be affecting youth of underrepresented
racial, ethnic, and linguistic backgrounds (i.e., REL minority youth) in particular. Trends in suicide attempts
over the past decade have remained higher or increased among Black and Latinx youth compared to white
peers. Machine learning (ML) and natural language processing (NLP) with electronic health records (EHR)
have advanced suicide risk identification, with potential to fill the need for sustainable and scalable practice-
based resources with near-term impact. Despite the near-term promise of ML and NLP with EHR data for
advancing suicide risk prediction, several important issues need to be addressed before ML-derived tools can
be broadly and optimally implemented in practice-based settings. Of particular concern is that such ML
algorithms may worsen health disparities in that they may lead to improvements in care for majority
populations without corresponding advancements for REL minority populations insofar as minority populations
are underrepresented in the development of these ML algorithms. Within this context, algorithms trained on
EHR data linked to geocoded data of social determinants of health (SDOH) and computerized adaptive testing
(CAT) has potential to improve suicide risk prediction. The portability (and thus scalability) of ML algorithms
across settings needs also to be demonstrated. Our current objective is therefore to develop EHR-derived ML
suicide risk algorithms that (i) minimize bias against underserved groups; (ii) are portable across settings; and
(iii) incorporate low-burden indices of general and population-relevant suicide risk. We will develop ML
algorithms with EHR data for prospective prediction of suicide risk for REL minority youth within 3 months of
inpatient discharge across 2 psychiatric inpatient services; determine the portability of these algorithms across
the 2 sites; and evaluate the added predictive value of SDOH and CAT data collected as part of standard care.
Our first aim is to develop prediction algorithms using EHR data to classify risk for STBs within 3 months of
discharge. We will evaluate the relative performance of co-trained versus locally trained ML algorithms from
two busy pediatric psychiatric inpatient sites to assess their portability. Our second aim is to evaluate the
incremental value of geocode-derived SDOH and CAT data in classifying suicide risk in REL minority youth. As
an exploratory aim, we will evaluate the performance of the algorithms developed in Aims 1 and 2 for specific
REL minority subsamples. The current project directly addresses NIMH/National Action Alliance for Suicide
Prevention’s Aspirational Goal to improve evaluation of suicide risk among diverse populations and in diverse
settings through feasible and scalable assessment strategies. It does so by focu...

## Key facts

- **NIH application ID:** 10904643
- **Project number:** 5P50MH129699-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Richard T Liu
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $248,692
- **Award type:** 5
- **Project period:** 2023-08-08 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10904643

## Citation

> US National Institutes of Health, RePORTER application 10904643, Improving Suicide Risk Prediction in Racial, Ethnic, and Linguistic Minority Youth (5P50MH129699-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10904643. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*