The Role of Big Data Analytics in Predicting Suicide

Kessler, Ronald C.; Bernecker, Samantha L.; Bossarte, Robert M.; Luedtke, Alex R.; McCarthy, John F.; Nock, Matthew K.; Pigeon, Wilfred R.; Petukhova, Maria V.; Sadikova, Ekaterina; VanderWeele, Tyler J.; Zuromski, Kelly L.; Zaslavsky, Alan M.

doi:10.1007/978-3-030-03553-2_5

Cited by 44Suicide and Self-Harm StudiesMachine Learning in HealthcareArtificial Intelligence in Healthcare

The Role of Big Data Analytics in Predicting Suicide

Kessler, Ronald C., Bernecker, Samantha L., Bossarte, Robert M., Luedtke, Alex R., McCarthy, John F., Nock, Matthew K., Pigeon, Wilfred R., Petukhova, Maria V. +4 more·2019·Personalized Psychiatry

DOI 10.1007/978-3-030-03553-2_5

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Machine learning methods have considerable promise for predicting suicide, although prior studies were limited in a number of ways.
“Results showed that ML methods have considerable promise even though all these studies were limited in a number of ways discussed elsewhere (71).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [71]
ML methods with a single classifier were used in most recent studies aimed at building suicide prediction models in high-risk patient populations using predictors that included patient self-report scales, clinical rating scales, and administrative data.
“As reviewed elsewhere, ML methods with a single classifier were used in most recent studies aimed at building suicide prediction models in these high-risk patient populations using predictors that included information collected from patient self-report scales, clinical rating scales, and administrative data (71).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [71]
It is unclear whether hospitalization (which occurs after 50% of VHA outpatient SAs) reduces subsequent SRBs (either suicide or subsequent SAs).
“As detailed elsewhere (71), it is unclear whether hospitalization (which occurs after 50% of VHA outpatient SAs) reduces subsequent SRBs (either suicide or subsequent SAs).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [71]

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Suicide prediction models: a critical review of recent research with recommendations for the way forward
Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M., & Zubizarreta, J. R. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0531-0 · Cited by 234 · see citing works
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Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models
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Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System
Kessler, R. C., Bauer, M. S., Bishop, T. M., Demler, O. V., Dobscha, S. K., Gildea, S. M., Goulet, J. L., Karras, E., Kreyenbuhl, J., Landes, S. J., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00390 · Cited by 52 · see citing works
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Authors: Kessler, R. C., Bernecker, S. L., Bossarte, R. M., Luedtke, A. R., McCarthy, J. F., Nock, M. K., Pigeon, W. R., Petukhova, M. V., Sadikova, E., VanderWeele, T. J., Zuromski, K. L., & Zaslavsky, A. M.
Affiliations: Harvard University (US)
West Virginia University (US)
Fred Hutch Cancer Center (US)
VA Center for Clinical Management Research (US)
Corresponding author: Kessler, Ronald C.
ORCID: Kessler, Ronald C.
Bernecker, Samantha L.
Bossarte, Robert M.
Luedtke, Alex R.
Nock, Matthew K.
Pigeon, Wilfred R.
Petukhova, Maria V.
Sadikova, Ekaterina
VanderWeele, Tyler J.
Zuromski, Kelly L.
Zaslavsky, Alan M.
Venue: Personalized Psychiatry
Publisher: Springer International Publishing
Published: 2019
Pages: 77-98
DOI: 10.1007/978-3-030-03553-2_5
OpenAlex: W2912751061
Cited by: 44
References: 101
Citations by year: 2025: 2 · 2024: 9 · 2023: 3 · 2022: 8 · 2021: 6 · 2020: 8 · 2019: 6 · 2018: 1
FWCI: 49.69
Type: book chapter
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Topics: Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: suicide risk prediction, big data analytics, electronic health records, machine learning, clinical evaluation, treatment planning

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