Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning
This study demonstrates that psychiatric self-report scales can effectively predict suicidal ideation risk in community-dwelling older adults. By utilizing efficient features, the predictive accuracy of the model can be enhanced, offering valuable insights for developing early identification systems for high-risk groups. These findings suggest that a community-based suicide prevention program could be promoted by implementing a screening system to identify individuals at high risk for suicidal ideation among the elderly.
Authors
- Hyun Ju Lim (ORCID: https://orcid.org/0000-0002-8962-8173)
- Hwagyu Suh (ORCID: https://orcid.org/0000-0002-0360-6708)
- Byung Dae Lee (ORCID: https://orcid.org/0000-0003-4295-8463)
- Eunsoo Moon (ORCID: https://orcid.org/0000-0002-8863-3413)
- Min Yoon (ORCID: https://orcid.org/0000-0002-6124-9163)
- Myung‐Jun Shin (ORCID: https://orcid.org/0000-0003-4010-0383)
- Kyungwon Kim (ORCID: https://orcid.org/0000-0001-8947-9078)
- Tae Sik Goh (ORCID: https://orcid.org/0000-0003-1861-5647)
- Jong‐Hwan Park (ORCID: https://orcid.org/0000-0003-2815-7248)
- Young Min Lee (ORCID: https://orcid.org/0000-0001-7483-7143)
Institutions
- Pusan National University Hospital (KR)
- Pusan National University Yangsan Hospital (KR)
- Pusan National University (KR)
- Pukyong National University (KR)
Publication Details
- Journal
- Clinical Psychopharmacology and Neuroscience
- Published
- 2025-08-12
- DOI
- https://doi.org/10.9758/cpn.25.1291
- Primary Topic
- Health and Wellbeing Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pusan National University
- Busan Metropolitan City