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Cited by 150Suicide and Self-Harm StudiesMachine Learning in HealthcareMental Health via Writing

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

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Tran, Truyen, Luo, Wei, Phung, Dinh, Harvey, Richard, Berk, Michael, Kennedy, Richard Lee, Venkatesh, Svetha·2014·BMC Psychiatry
DOI 10.1186/1471-244x-14-76PMC PMC3984680
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Abstract

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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Metadata

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
ORCID
Venue
BMC Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2014-03-14
Volume
14
Issue
1
Pages
76-76
ISSN
1471-244X (ISSN-L)
DOI
10.1186/1471-244x-14-76
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
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

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