Development and validation of machine learning-based prediction of depression progression using EHR data: a multi-institutional retrospective cohort study

Abstract Depression is common and often difficult to monitor for clinical worsening in real-world settings where structured symptom scales are inconsistently captured. Electronic health records allow large cohort analyses. Diagnostic-code based definitions offer a practical way to identify patients at risk for progression. We used a multi-institutional dataset (MedStar Health) to study adults with mild depression and evaluated whether machine learning models could predict progression to moderate or severe depression within two years. The cohort included 803 individuals with two years of continuous follow-up. A broad set of demographics, clinical, laboratory, socioeconomic, and healthcare utilization features was available. Models included logistic regression, random forest, gradient boosted trees, and a deep neural network (DNN). Gradient boosted trees showed balanced performance, defined as a trade-off between sensitivity and specificity with a modest improvement over baseline prevalence, while logistic regression demonstrated the strongest overall discrimination and generalization. The DNN showed lower accuracy than null classifier. These findings demonstrate that real-world EHR data can support risk prediction of depression worsening with stable and clinically relevant performance. These models may support earlier identification of patients who warrant closer monitoring or targeted follow-up.