acePredicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study
Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatients experiencing readmission within 90 days after discharge. Routinely collected clinical information may help identify individuals at higher risk of readmission. Machine learning (ML) models could complement more traditional statistical approaches by exploring complex relationships among multiple vulnerability factors. Methods The DEEP READ study is a prospective, multicentre cohort study conducted across…
A Random Forest trained on 22 routine clinical variables predicted unplanned 90-day psychiatric readmission in adults with MDD with moderate discrimination.
The model showed variable cross-validation performance down to 0.59 AUC and is not validated for clinical implementation without further calibration and utility assessment.
Model was only internally evaluated with stratified 5-fold cross-validation and lacks external validation, calibration assessment, and demonstrated clinical utility.
Evidence
- Peer-reviewedJournal of Affective Disorders2026-09-13
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Truvace Impact Record TRV-2026-1096, v1: “Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study.” Truvace, 2026-09-15. /record/TRV-2026-1096 (accessed at citation time). sha256 cc8d484b8b1f7225…
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