The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Predicting suicidal behavior from longitudinal electronic health records.
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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“multiple research groups have developed suicide risk prediction models using electronic health record data.10,11,12,13”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [11]
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“33%, from a model developed in Massachusetts academic medical centers11”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [11]
In the citing paper
“and 2% to 6%11,12 for predicting suicide attempts”
From Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US (2021) · cites this paper as [11]
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Metadata
- 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)
- Venue
- American Journal of Psychiatry
- Published
- 2017-02-01
- Volume
- 174
- Issue
- 2
- Pages
- 154-162
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- 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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