Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

ABSTRACT: 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 processing method that evaluates topic changes over time, to analyze high-suicide risk Veterans Affairs patients' unstructured electronic health records. Our sample included all high-risk patients that died (cases) or did not (controls) by suicide in 2017 and 2018. Cases and controls shared the same risk, location, and treatment intervals and received nine months of mental health care during the year before the relevant end date. Each case was matched with five controls. We analyzed case records from diagnosis until death and control records from diagnosis until matched case's death date. Our final sample in

SUBMITTER: Levis M 

PROVIDER: S-EPMC11488589 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature