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Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A

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    Area of Science:

    • Clinical informatics
    • Machine learning in healthcare
    • Depression research

    Background:

    • Depression is a major global health issue, with timely identification of worsening cases being a significant challenge.
    • Electronic health records (EHRs) offer valuable data for real-world disease trajectory analysis.
    • Lack of standardized symptom scales in EHRs necessitates alternative methods like ICD10 code-based progression for predictive modeling.

    Purpose of the Study:

    • To develop and evaluate machine learning (ML) and deep learning (DL) models for predicting depression severity progression.
    • Utilize International Classification of Diseases, 10th Revision (ICD10) codes within EHR data for predicting mild to moderate/severe depression.
    • Leverage data from the MedStar Health Research Institute (MHRI) EHR database for model development and validation.

    Main Methods:

    • Retrospective cohort analysis of adult patients diagnosed with mild depression (ICD10) between 2017-2023.
    • Inclusion of a heterogeneous feature set including demographics, socioeconomic factors, and healthcare utilization.
    • Development and comparison of logistic regression, random forest, XGBoost, CatBoost, and deep neural network (DNN) models with a structured model selection framework.

    Main Results:

    • The analytic cohort comprised 803 patients with two-year follow-up; DNN was excluded for not meeting AUC thresholds.
    • XGBoost achieved the highest composite score (Accuracy=0.72, AUC=0.776, Sensitivity=0.77), demonstrating robust predictive performance.
    • Logistic regression performed closely, with other models like Random Forest being penalized for overfitting.

    Conclusions:

    • Machine learning models, particularly XGBoost, can effectively predict depression progression using routinely collected EHR data.
    • The study highlights the feasibility of using socioeconomic and EHR data for early identification of worsening depression.
    • Emphasizes the need for transparent model selection frameworks to ensure trustworthy clinical AI applications.