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

Depression and Mood Disorders; Suicide Prevention (1364) Clinical Information and Decision Making (1411) Decision Support for Health Professionals (1191) Electronic Health Records (1058)

Published on in Vol 3, No 3 (2016): Jul-Sept

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Authors of this article:

Chandan Karmakar1 Author Orcid Image ;   Wei Luo1 Author Orcid Image ;   Truyen Tran1 Author Orcid Image ;   Michael Berk2 Author Orcid Image ;   Svetha Venkatesh1 Author Orcid Image
Article Authors Cited by (24) Tweetations (16) Metrics

Journals

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  14. Edgcomb J, Thiruvalluru R, Pathak J, Brooks J. Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness. Medical Care 2021;59:S58 View
  15. Maruta N, Yaroslavcev S, Kalenskaya G, Oprya Y, Korop O, Denysenko M, Zavorotniy V. PHENOMENOLOGICAL ANALYSIS OF SUICIDAL BEHAVIOR IN PATIENTS WITH COGNITIVE IMPAIRMENT IN RECURRENT DEPRESSIVE DISORDER. Wiadomości Lekarskie 2022;75(1):293 View
  16. Horowitz L, Tipton M, Pao M. Primary and Secondary Prevention of Youth Suicide. Pediatrics 2020;145(Supplement_2):S195 View
  17. Sheu Y, Sun J, Lee H, Castro V, Barak-Corren Y, Song E, Madsen E, Gordon W, Kohane I, Churchill S, Reis B, Cai T, Smoller J. An efficient landmark model for prediction of suicide attempts in multiple clinical settings. Psychiatry Research 2023;323:115175 View
  18. Kim D, Quan L, Seo M, Kim K, Kim J, Zhu Y. Interpretable machine learning‐based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data. Suicide and Life-Threatening Behavior 2023;53(3):484 View
  19. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502 View
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  21. Somé N, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Frontiers in Psychiatry 2024;15 View
  22. Kim D, Jiang T, Baek J, Jang S, Zhu Y. Understanding and comparing risk factors and subtypes in South Korean adult and adolescent women's suicidal ideation or suicide attempt using survey and social media data. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

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  2. Larsen M, Vo L, Pratap A, Peters D. Tasman’s Psychiatry. View