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A Machine Learning Approach to Predicting Treatment Outcomes in Bipolar Depression with OCD Comorbidity

DOI: 10.4236/oalib.1112894, PP. 1-20

Subject Areas: Psychiatry & Psychology, Computer Engineering, Artificial Intelligence

Keywords: Bipolar Depression, Obsessive-Compulsive Disorder (OCD), Machine Learning (ML), XGBoost, Treatment Response Prediction, SHAP (SHapley Ad-ditive exPlanations)

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Abstract

Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high relapse rates. Traditional ap-proaches to treatment planning lack reliable tools for predicting pa-tient-specific outcomes, leaving clinicians with limited options for personal-izing care. This study leverages advanced machine learning (ML), specifical-ly XGBoost, to develop a predictive framework capable of classifying treat-ment responses while identifying key predictors such as age, clinical scores (HDRS, YBOCS), and treatment characteristics (quetiapine dose). By incor-porating interpretability techniques such as SHAP (SHapley Additive exPla-nations), the model provides transparent insights into how individual fea-tures influence predictions, making the outputs actionable for clinical deci-sion-making. Furthermore, probabilistic predictions are evaluated and cali-brated using isotonic regression to ensure reliability, particularly for high-stakes applications in psychiatry. Through detailed visual analyses, in-cluding confusion matrices, ROC-AUC curves, SHAP plots, and calibration curves, this research bridges the gap between data-driven methodologies and clinical practice, offering a robust framework for advancing personalized treatment strategies in bipolar depression with OCD comorbidity.

 Cite this paper

Filippis, R. D. and Foysal, A. A. (2025). A Machine Learning Approach to Predicting Treatment Outcomes in Bipolar Depression with OCD Comorbidity. Open Access Library Journal, 12, e2894. doi: http://dx.doi.org/10.4236/oalib.1112894.

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