Using machine learning approach to predict suicide ideation and suicide attempts among Chinese adolescents: A cross-sectional study.

Journal: Journal of affective disorders

PMID: 40288453

Abstract

BACKGROUND: Screening for suicide ideation and suicide attempts is crucial for adolescents, yet accurately predicting these outcomes remains a significant challenge. The relationship between non-suicidal self-injury and suicide ideation and attempts is complex. Therefore, this study employs machine learning techniques to explore and address these challenges.

Authors

Xin Yu

eSep Inc., Keihanna Open Innovation Center @ Kyoto (KICK), Annex 320, 7-5-1, Seikadai, Seika-cho, Soraku-gun, Kyoto 619-0238, Japan.
Liyan Wang

School of Computer and Information Technology, Liaoning Normal University, 116081, China.
Pengxiang Sun

China Telecom Tianyi Cloud Technology Co., Ltd., Chengdu 610000, China.
Yanru Zhang

Department of Hospital Administration Office, Xingtai Central Hospital, Xingtai, China.
Xu Chen

School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.

Keywords

Adolescent Child China Cross-Sectional Studies East Asian People Female Humans Machine Learning Male Self-Injurious Behavior Suicidal Ideation Suicide, Attempted

External Resources

View on PubMed Access via DOI PubMed (40288453)

Using machine learning approach to predict suicide ideation and suicide attempts among Chinese adolescents: A cross-sectional study.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals