Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions.

ABSTRACT: Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data. Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior. Re

SUBMITTER: Boudreaux ED 

PROVIDER: S-EPMC8369059 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature