Background: Suicide risk prediction models frequently rely on structured electronic health
record (EHR) data, including patient demographics and health care usage variables …

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

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

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 …

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients.
Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language …

Background Suicide risk prediction models have been developed by using information from
patients' electronic health records (EHR), but the time elapsed between model development …

Despite recognizing the critical association between social and behavioral determinants of
health (SBDH) and suicide risk, SBDHs from unstructured electronic health record (EHR) …

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

Objective: The objective of this study was to investigate the impact of enhancing a structured-
data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers …

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 …