Cited by 339Suicide and Self-Harm StudiesMachine Learning in HealthcareMental Health via Writing

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Predicting suicidal behavior from longitudinal electronic health records.

Barak-Corren, Yuval, Castro, Victor M., Javitt, Solomon, Hoffnagle, Alison G., Dai, Yael, Perlis, Roy H., Nock, Matthew K., Smoller, Jordan W., Reis, Ben Y.·2017·American Journal of Psychiatry

DOI 10.1176/appi.ajp.2016.16010077

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Abstract

OBJECTIVE: The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients' future risk of suicidal behavior. METHOD: Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998-2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N=1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set. RESULTS: Among the study population, 1.2% (N=20,246) met the case definition for suicidal behavior. The model achieved sensitive (33%-45% sensitivity), specific (90%-95% specificity), and early (3-4 years in advance on average) prediction of patients' future suicidal behavior. The strongest predictors identified by the model included both well-known (e.g., substance abuse and psychiatric disorders) and less conventional (e.g., certain injuries and chronic conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk profiles. CONCLUSIONS: Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.

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Authors: Barak-Corren, Y., Castro, V. M., Javitt, S., Hoffnagle, A. G., Dai, Y., Perlis, R. H., Nock, M. K., Smoller, J. W., & Reis, B. Y.
Affiliations: From the Predictive Medicine Group, Boston Children’s Hospital Informatics Program, Boston; the Technion, Israeli Institute of Technology, Haifa, Israel; the Partners Research Information Systems and Computing, Boston; the Department of Psychiatry, Massachusetts General Hospital, Boston; the Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston; the Department of Psychology, Harvard University, Boston; and Harvard Medical School, Boston.
Boston Children's Hospital (US)
ORCID: Castro, Victor M.
Dai, Yael
Perlis, Roy H.
Nock, Matthew K.
Smoller, Jordan W.
Reis, Ben Y.
Venue: American Journal of Psychiatry
Publisher: American Psychiatric Association Publishing
Published: 2017-02-01
Volume: 174
Issue: 2
Pages: 154-162
ISSN: 0002-953X (ISSN-L)
1535-7228
DOI: 10.1176/appi.ajp.2016.16010077
OpenAlex: W2509888018
Cited by: 339
References: 26
Citations by year: 2026: 23 · 2025: 43 · 2024: 32 · 2023: 41 · 2022: 38 · 2021: 48 · 2020: 37 · 2019: 41 · 2018: 26 · 2017: 10
FWCI: 34.32
Type: journal article
Language: en
Open access: closed
Locations: American Journal of Psychiatry (publishedVersion)
PubMed (publishedVersion)
Funders: Pfizer
Massachusetts General Hospital
Tommy Fuss Fund
Harvard University
Department of Psychology, Harvard University
National Institute of Mental Health (K24MH094614)
Topics: Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords: suicidal behavior, suicide risk prediction, electronic health records, suicide attempts, suicide mortality, risk factors, substance abuse, mental illness, injuries, chronic disease, early warning systems, risk screening
MeSH terms: Adult, Aged, Mental Disorders, Female, Humans, Longitudinal Studies, Male, Massachusetts, Middle Aged, Registries, Suicide, Suicide, Attempted, Case-Control Studies, Risk Assessment, Substance-Related Disorders, Electronic Health Records

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Predicting suicidal behavior from longitudinal electronic health records.

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