Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data.

ABSTRACT:

Background

Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

Methods

EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g. diagnoses, medication, and coercive measures) and 1134 predictors from free text using term frequency-inverse document frequency and sentence transformers.

SUBMITTER: Perfalk E 

PROVIDER: S-EPMC11650168 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature