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

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 included 218 cases and 943 controls. We analyzed the corpus using a Python-based Dynamic Topic Modeling algorithm. We identified five distinct topics, "Medication," "Intervention," "Treatment Goals," "Suicide," and "Treatment Focus." We observed divergent change patterns over time, with pathology-focused care increasing for cases and supportive care increasing for controls. The case topics tended to fluctuate more than the control topics, suggesting the importance of monitoring lability. Our study provides a method for monitoring risk fluctuation and strengthens the groundwork for time-sensitive risk measurement.

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Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Maxwell Levis reports financial support was provided by VA New England Healthcare System. Joshua Levy reports financial support was provided by the Department of Defense Award (HT9425-23-1-0267). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.