The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Predicting Risk of Suicide Attempts Over Time Through Machine Learning
Abstract
Traditional approaches to the prediction of suicide attempts have limited the accuracy and scale of risk detection for these dangerous behaviors. We sought to overcome these limitations by applying machine learning to electronic health records within a large medical database. Participants were 5,167 adult patients with a claim code for self-injury (i.e., ICD-9, E95x); expert review of records determined that 3,250 patients made a suicide attempt (i.e., cases), and 1,917 patients engaged in self-injury that was nonsuicidal, accidental, or nonverifiable (i.e., controls). We developed machine learning algorithms that accurately predicted future suicide attempts (AUC = 0.84, precision = 0.79, recall = 0.95, Brier score = 0.14). Moreover, accuracy improved from 720 days to 7 days before the suicide attempt, and predictor importance shifted across time. These findings represent a step toward accurate and scalable risk detection and provide insight into how suicide attempt risk shifts over time.
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Cites
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Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017) · Psychological Bulletin · doi:10.1037/bul0000084 · Cited by 3,784
Self-citationArea Deprivation and Widening Inequalities in US Mortality, 1969–1998
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Self-citationTowards an integrated motivational–volitional model of suicidal behaviour
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“Although short-term risk is a major part of many suicide theories (e.g., O’Connor, 2011; Wenzel & Beck, 2008) and a major focus of clinical guidelines (e.g., Rudd et al., 2006), few studies have examined short-term risk for suicide attempts.”
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Cited by
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
The integrated motivational–volitional model of suicidal behaviour
O'Connor, R. C., & Kirtley, O. J. (2018) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2017.0268 · Cited by 1,781
Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956
Ribeiro, J. D., Huang, X., Fox, K. R., & Franklin, J. C. (2018) · British Journal of Psychiatry · doi:10.1192/bjp.2018.27 · Cited by 894
Self-citationAnnual Research Review: Suicide among youth – epidemiology, (potential) etiology, and treatment
Cha, C. B., Franz, P. J., M. Guzmán, E., Glenn, C. R., Kleiman, E. M., & Nock, M. K. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12831 · Cited by 602
Metadata
- Authors
- Walsh, C. G., Ribeiro, J. D., & Franklin, J. C.
- Venue
- Clinical Psychological Science
- Publisher
- SAGE Publications
- Published
- 2017-04-11
- Volume
- 5
- Issue
- 3
- Pages
- 457-469
- ISSN
- 2167-7026 (ISSN-L)
- 2167-7034
- OpenAlex
- W2605512411
- Cited by
- 567
- References
- 26
- FWCI
- 64.17
- Type
- journal article
- Language
- en
- Open access
- closed
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