Citation

Levis M, Levy J, Dufort V, Russ CJ, Shiner B. Clin. Psychol. Psychother. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, John Wiley and Sons)

DOI

10.1002/cpp.2842

PMID

36797651

Abstract

In the machine learning subfield of Natural Language Processing (NLP), a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modeling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record (EHR) psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004-2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77, controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication, and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity, and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial, and time-sensitive suicide risk variables.

Language: en

Keywords

dynamic topic models; electronic medical records; natural language processing; suicide prediction