Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.
Journal:
BMC psychiatry
Published Date:
Aug 29, 2025
Abstract
BACKGROUND: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, effectively identifying those most likely to re-attempt is a persistent difficulty, especially when public health resources are limited. This creates a pressing need for accurate and practical risk assessment tools. This study aims to address this gap by using machine learning to analyze a nationwide suicide surveillance database to identify key predictors of repeat suicide attempts and develop a robust predictive model to aid in resource allocation and early intervention.
