This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1363) Clinical Information and Decision Making (1407) New Methods (944) Methods and Feasibility Studies (379) Decision Support for Health Professionals (1185) Electronic Health Records (1053) Diagnostic Tools in Mental Health (242) Machine Learning (1465) Special Issue on Computing and Mental Health (2018) (33)

Published on in Vol 5, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10144, first published .
Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Authors of this article:

Marcos DelPozo-Banos1 Author Orcid Image ;   Ann John1 Author Orcid Image ;   Nicolai Petkov2 Author Orcid Image ;   Damon Mark Berridge1 Author Orcid Image ;   Kate Southern3 Author Orcid Image ;   Keith LLoyd1 Author Orcid Image ;   Caroline Jones4 Author Orcid Image ;   Sarah Spencer5 Author Orcid Image ;   Carlos Manuel Travieso6 Author Orcid Image
Article Authors Cited by (35) Tweetations (60) Metrics

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  16. Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. Adolescent Psychiatry 2022;12(1):1 View
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  23. Baniadamdizaj S, Baniadamdizaj S. Prediction of Iranian EFL teachers' burnout level using machine learning algorithms and maslach burnout inventory. Iran Journal of Computer Science 2023;6(1):1 View
  24. Zhang J, Liang S, Liu X, Li D, Zhou F, Xiao L, Liu J, Sha S. Factors associated with suicidal attempts in female patients with mood disorder. Frontiers in Public Health 2023;11 View
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Books/Policy Documents

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