Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning

INTRODUCTION: Recurrent psychiatric hospitalizations in youth are costly and burdensome to patients and families. While postdischarge planning rarely stratifies patients based on their risk of readmission, predictive modeling could identify at-risk youth to facilitate targeted intervention strategies. This study developed and validated machine learning models to predict psychiatric readmission in children and adolescents. METHODS: This retrospective cohort study included 11,225 patients (20,339 psychiatric admissions) ≤ 18 years old with at least one psychiatric admission and an anxiety or depressive disorder diagnosis at any time during the study period from two tertiary-care academic medical centers. Machine learning models were developed to predict psychiatric readmission versus no readmission within 30, 90, and 180 days of discharge using electronic health record data from one institution. These models were internally validated at the development site and externally validated at the second institution. RESULTS: Model development and internal validation using random forest models predicted 30-, 90-, and 180-day readmission with area under the receiver operating characteristic curves (AUROC) equal to 0.739, 0.742, and 0.746, respectively. The most important features identified across follow-up periods included previous psychiatric admissions, length of stay, age, and antipsychotic prescriptions. Models trained using a reduced set of 18 features obtained similar performance, with AUROCs ranging from 0.741 to 0.745. External validation of the models retrained using 12 features available at both institutions yielded AUROCs of 0.598, 0.634, and 0.657 for 30-, 90-, and 180-day readmission, respectively. CONCLUSION: The findings of this study suggest that machine learning models can identify children and adolescents at risk of psychiatric readmission.