Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data
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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.
Objectives
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 1,134 predictors from free text using term frequency - inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the two years prior, we predicted involuntary admission 180 days ahead. XGBoost and Elastic Net regularized logistic regression models were trained on 85% of the dataset. The best performing model was tested on the remaining 15% of the data.
Results
The model was trained on 50,634 voluntary inpatient discharges among 17,968 unique patients. The cohort comprised 1,672 voluntary inpatient discharges followed by an involuntary admission. The XGBoost model performed best in the training phase and obtained an area under the receiver operating curve of 0.84 in the test phase.
Conclusion
A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.
