Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps-Reference-Cited by-同舟云学术

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps

Published:2018-06-28 Issue:7 Volume:20 Page:
ISSN:1523-3812
Container-title:Current Psychiatry Reports
language:en
Short-container-title:Curr Psychiatry Rep

Author:

Torous John,Larsen Mark E.,Depp Colin,Cosco Theodore D.,Barnett Ian,Nock Matthew K.,Firth Joe

Publisher

Springer Science and Business Media LLC

Subject

Psychiatry and Mental health

Link

http://link.springer.com/article/10.1007/s11920-018-0914-y/fulltext.html

Reference42 articles.

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2. Bernecker SL, Rosellini AJ, Nock MK, Chiu WT, Gutierrez PM, Hwang I, et al. Improving risk prediction accuracy for new soldiers in the US Army by adding self-report survey data to administrative data. BMC Psychiatry. 2018;18(1):87.

3. Stein MB, Ware EB, Mitchell C, Chen CY, Borja S, Cai T, et al. Genomewide association studies of suicide attempts in US soldiers. Am J Med Genet B Neuropsychiatr Genet. 2017;174(8):786–97.

4. Larsen ME, Cummins N, Boonstra TW, O’Dea B, Tighe J, Nicholas J, Shand F, Epps J, Christensen H. The use of technology in suicide prevention. Proceedings of the 37th Annual Conference of the Engineering in Medicine and Biology Conference (EMBC). 2015. 7316–7319.

5. Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bull. 2017;143(2):187.

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