Identifying high-risk elderly for suicide using machine learning
Dados Bibliográficos
| ID | 21541506 |
|---|---|
| Autores | Hwanoong Lee (Konkuk University), Kahyun Lee (0000-0002-5497-8114), Kwanhyong Lee (0000-0003-0839-7498, Hongik University, autor correspondente) |
| Ano | 2025 |
| Volume | 69 |
| Fascículo | 4 |
| Páginas | 2467-2499 |
| Data de publicação | 2025-10-01 |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Empirical Economics (JOURNAL) |
| Identificadores do periódico | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Editora | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-025-02780-7 |
| OpenAlex | W4411607405 |
| Idioma | EN |
| Referências citadas | 33 |
Actuarial science · Econometrics · Economics · Machine learning · Medical emergency · Poison control · Suicide prevention · Suicide Risk · Computer Science · Medicine · Mental Health via Writing · Psychology · Substance Abuse Treatment and Outcomes · Suicide and Self-Harm Studies · Artificial Intelligence
Human Decisions and Machine Predictions
The Three-Step Theory (3ST)
Smote
Using Machine Learning to Create an Early Warning System for Welfare Recipients
Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults
Development of a Suicide Prediction Model for the Elderly Using Health Screening Data
Machine Learning
| Altamente citado | Não |
|---|