Electronic medical record (EMR)-based suicide risk prediction methods typically rely on
analysis of structured variables such as demographics, visit history, and prescription data …

We developed linguistics-driven prediction models to estimate the risk of suicide. These
models were generated from unstructured clinical notes taken from a national sample of US …

Objective Limited research exists in predicting first-time suicide attempts that account for two-
thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data …

BackgroundThis study evaluated whether natural language processing (NLP) of
psychotherapy note text provides additional accuracy over and above currently used suicide …

The onset and persistence of life events (LE) such as housing instability, job instability, and
reduced social connection have been shown to increase risk of suicide. Predictive models …

Suicide risk prediction models can identify individuals for targeted intervention. Discussions
of transparency, explainability, and transportability in machine learning presume complex …

Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in
Electronic Health Records (EHRs) at scale are suboptimal because suicide-related …

Clinical risk prediction models powered by electronic health records (EHRs) are becoming
increasingly widespread in clinical practice. With suicide-related mortality rates rising in …

We present an ensemble transfer learning method to predict suicide from Veterans Affairs
(VA) electronic medical records (EMR). A diverse set of base models was trained to predict a …

Importance Suicide is a leading cause of mortality, with suicide-related deaths increasing in
recent years. Automated methods for individualized risk prediction have great potential to …