Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.
Saved in:
| Title: | Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach. |
|---|---|
| Authors: | Park C; Division of Digital Healthcare, Yonsei University, Wonju, South Korea.; Center for Planetary Health Digital Healthcare, Institute for Planetary Health, Yonsei University, Wonju, South Korea., Lee BC; Department of Health and Human Performance, University of Houston, Houston, Texas, United States of America.; Center for Neuromotor and Biomechanics Research, University of Houston, Houston, Texas, United States of America. |
| Source: | PloS one [PLoS One] 2026 Apr 06; Vol. 21 (4), pp. e0346050. Date of Electronic Publication: 2026 Apr 06 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1932-6203 |
|---|---|
| DOI: | 10.1371/journal.pone.0346050 |
