Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments
Open accessAbstract
BACKGROUND: To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month risk. METHODS: 7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator). RESULTS: In the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 - 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 - 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83). CONCLUSION: Predictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.
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Numerous recent studies predicted suicide death or attempted suicide in high-risk patient populations from electronic health record data using machine learning methods.
“Numerous recent studies predicted suicide death or attempted suicide in high-risk patient populations from electronic health record data using machine learning (ML) methods.”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [69]
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Will Big Data and personalized medicine do the gender dimension justice?
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Predicting Future Suicide: Clinician Opinion versus a Standardized Assessment Tool
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What We Can Learn From Failure: An EHR-Based Child Protection Alert System
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Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives
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- Authors
- Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., & Venkatesh, S.
- Affiliations
- Deakin University (AU)
- Barwon Health (AU)
- Corresponding author
- Venkatesh, Svetha
- Venue
- BMC Psychiatry
- Published
- 2014-03-14
- Volume
- 14
- Issue
- 1
- Pages
- 76-76
- ISSN
- 1471-244X (ISSN-L)
- PMC
- PMC3984680
- OpenAlex
- W2123409929
- Cited by
- 150
- References
- 41
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 9 · 2022: 8 · 2021: 32 · 2020: 17 · 2019: 12 · 2018: 14 · 2017: 14 · 2016: 10 · 2015: 7 · 2014: 1
- FWCI
- 12.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eSpace (Curtin University) (submittedVersion)
- eSpace (Curtin University) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- 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
- suicide risk, risk stratification, electronic health records, clinical risk assessment, prediction model, emergency department visits, hospitalization, health care utilization, LASSO
- MeSH terms
- Suicide Prevention, Adolescent, Adult, Aged, Australia, Emergency Service, Hospital, Female, Humans, Male, Medical History Taking, Middle Aged, Prognosis, Retrospective Studies, Risk Factors, Suicide, Models, Statistical, Risk Assessment, Young Adult, Electronic Health Records, Suicidal Ideation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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