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Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study
Bumjo Oh, Je-Yeon Yun, Eun Chong Yeo, Dong-Hoi Kim, Jin Kim, Bum-Joo Cho
Psychiatry Investig. 2020;17(4):331-340. Published online 2020 Mar 27 DOI: https://doi.org/10.30773/pi.2019.0270
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Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons
Khayyam Akhtar, Muhammad Usman Yaseen, Muhammad Imran, Sohaib Bin Altaf Khattak, Moustafa M. Nasralla
PeerJ Computer Science.2024; 10: e2051. CrossRef Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning
Myoung Soo Kim, Jung Mi Ryu, Byung Kwan Choi
CIN: Computers, Informatics, Nursing.2023; 41(4): 236. CrossRef Factors Related to Suicidal Ideation by Gender and Age Group in Korean Adults
Eun Young Kim, Yong Whi Jeong, Jihye Lim, Dae Ryong Kang
Journal of Korean Medical Science.2023;[Epub] CrossRef Machine learning for suicidal ideation identification: A systematic literature review
Wesllei Felipe Heckler, Juliano Varella de Carvalho, Jorge Luis Victória Barbosa
Computers in Human Behavior.2022; 128: 107095. CrossRef Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review
Nighat Z Khan, Muhammad Ali Javed
Cureus.2022;[Epub] CrossRef Suicidal behaviour prediction models using machine learning techniques: A systematic review
Noratikah Nordin, Zurinahni Zainol, Mohd Halim Mohd Noor, Lai Fong Chan
Artificial Intelligence in Medicine.2022; 132: 102395. CrossRef Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness – A Literature Review
Nighat Z. Khan, Muhammad Ali Javed
SSRN Electronic Journal .2022;[Epub] CrossRef
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