Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

ABSTRACT: Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-leve

SUBMITTER: Zheng L 

PROVIDER: S-EPMC7033212 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature