Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data
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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. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the 2 years prior, we predicted involuntary admission 180 days ahead. XGBoost and elastic net models were trained on 85% of the dataset. The models with the highest area under the receiver operating characteristic curve (AUROC) were tested on the remaining 15% of the data. Results: The model was trained on 50 634 voluntary inpatient discharges among 17 968 patients. The cohort comprised of 1672 voluntary inpatient discharges followed by an involuntary admission. The best XGBoost and elastic net model from the training phase obtained an AUROC of 0.84 and 0.83, respectively, 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.
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Machine learning approaches have previously been utilized to predict adverse mental health outcomes, including progression of schizophrenia and bipolar disorder and involuntary psychiatric admission.
“Machine learning approaches have previously been utilized to predict adverse mental health outcomes, including progression of schizophrenia and bipolar disorder [51] and involuntary psychiatric admission [52].”
From Factors associated with postpartum depression symptoms following antepartum hospitalization (2026) · cites this paper as [52]
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Factors associated with postpartum depression symptoms following antepartum hospitalization
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- Authors
- Perfalk, E., Damgaard, J. G., Bernstorff, M., Hansen, L., Danielsen, A. A., & Østergaard, S. D.
- Affiliations
- Aarhus University (DK)
- Corresponding author
- Perfalk, Erik
- ORCID
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024-11
- Volume
- 54
- Issue
- 15
- Pages
- 4348-4361
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- PMC
- PMC11650168
- OpenAlex
- W4404474356
- Cited by
- 12
- References
- 48
- Citations by year
- 2026: 7 · 2025: 5
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-11-18)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Psychological Medicine (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Lundbeckfonden (R344‐2020‐1073, R358-2020-2341, R283-A16461)
- Novo Nordisk (NNF20SA0062874)
- Novo Nordisk Fonden (NNF20SA0062874)
- Region Midtjylland
- H. Lundbeck A/S
- Topics
- Social Sciences › Psychology › Clinical Psychology › Healthcare Decision-Making and Restraints
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Keywords
- involuntary psychiatric admission, psychiatric inpatient care, prediction model, electronic health records, clinical decision support, coercion in mental health care, XGBoost, elastic net, risk prediction
- MeSH terms
- Machine Learning, Adult, Mental Disorders, Commitment of Persons with Psychiatric Disorders, Denmark, Female, Hospitals, Psychiatric, Humans, Inpatients, Male, Middle Aged, Patient Discharge, Electronic Health Records
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