Predicting imminent suicide risk in a crisis hotline chat using machine learning.

ABSTRACT: Identifying individuals at the highest risk of suicide in real time is one of the critical tasks in suicide prevention. However, understanding the mental processes of at-risk individuals is a formidable endeavor due to the rapid, impulsive, and temporally dynamic characteristics of severe suicide crises. Recent theories, such as the suicide crisis syndrome, have suggested sets of short-term predictors of imminent suicide risk (IMSR), defined as an acute state that requires urgent intervention to prevent suicide. However, to date, no study has examined the prediction value of these theories in real time. In this study, we used machine learning to investigate its potential for predicting IMSR during internet-based crisis hotline chat sessions. We analyzed 3309 anonymized chat sessions from a

SUBMITTER: Levi-Belz Y 

PROVIDER: S-EPMC12748772 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature