# Predicting suicide attempt in youth by integrating EHR, clinical, cognitive and imaging data

> **NIH NIH R21** · CHILDREN'S HOSP OF PHILADELPHIA · 2020 · $498,803

## Abstract

Summary. Suicide in youths is a growing health concern, yet current clinical practice falls short of timely
identifying youths at risk for suicide attempt (SA). The overarching aim of this research is to use data driven
machine learning methods to facilitate primary prevention of youth SAs in primary care pediatric settings.
Clinical guidelines recommend screening for depression, considered a proxy for suicide risk, from age 12 in
pediatric setting. The proposed study aims at identification of variables (features) that can be collected by early
adolescence, and contribute to prediction of SA in later adolescence. This study will leverage the effort that has
been invested in previous projects: a study using electronic health records (EHR) to predict SAs and deaths in
University of Pittsburgh Medical Center (UPMC) hospitals; and the Philadelphia Neurodevelopmental Cohort
(PNC), that included comprehensive phenotyping of ~9,500 youths. These previous efforts will be integrated to
develop and optimize SA prediction in youth from the Children’s Hospital of Philadelphia (CHOP) network, from
which we have data on ~40,000 who were screened for a history of SA between the years 2014-2018
(n~1500). First, in the CHOP dataset, we will generate predictive models based on UPMC data, test their
predictive validity in CHOP youth population, and then develop, optimize, and cross validate these predictive
models using CHOP EHR data as a training set (Aim 1). Second, in the PNC dataset, we will use multiple data
types (demographic, behavioral, cognitive, imaging) to classify youths with suicide ideation (SI, n~750) and
identify features (potentially modifiable) that are indicative of SI and may also point to potential mechanisms
underlying youth SI (Aim 2). Lastly, in a subset of 936 youths (49 with SAs) with both CHOP EHR data and
research PNC evaluation that was conducted at mean age 11 (T1), ~5 years before SA screening (T2), we will
test the validity of models from Aims 1&2, and aim to identify data features that were collected at T1 and can
improve/optimize/outperform the prediction of SAs that relies solely on EHR data (Aim 3). The proposed study
relies on the expertise of a highly capable multidisciplinary team comprised of Dr. Barzilay (PI), child-
adolescent psychiatrist experienced in suicide research and analysis of suicide related phenotypes in PNC
data; Dr. Tsui (PI), an expert in machine learning who has developed predictive algorithms of SA and deaths
using UPMC data; and collaborators critical for meeting study aims, Dr. Raquel Gur as the lead researcher
who established the PNC, Dr. Ruben Gur who developed the PNC neurocognitive assessment tools, and Dr.
Oquendo who will provide expertise in suicide prediction research. The team’s access and familiarity with
CHOP EHR and PNC data resources, coupled with its interdisciplinary expertise, creates a unique opportunity
to identify childhood features that can optimize later adolescent SA prediction. Expe...

## Key facts

- **NIH application ID:** 10038009
- **Project number:** 1R21MH123916-01
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Ran Barzilay
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $498,803
- **Award type:** 1
- **Project period:** 2020-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10038009

## Citation

> US National Institutes of Health, RePORTER application 10038009, Predicting suicide attempt in youth by integrating EHR, clinical, cognitive and imaging data (1R21MH123916-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10038009. Licensed CC0.

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