Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning

2025-03-13

AI Summary

This study addresses the clinical need for improved prediction of treatment response in body dysmorphic disorder (BDD). Leveraging multicenter clinical data, we developed and validated interpretable machine learning models—including logistic regression, support vector machines, and decision trees—that integrate baseline symptom severity, cognitive-behavioral features, and treatment-related variables. Our key contribution is the first identification of “treatment credibility” as the strongest modifiable predictor of treatment response—outperforming conventional predictors such as baseline symptom severity. Decision tree analysis further uncovered clinically meaningful thresholds (e.g., credibility score ≥7.2 predicting high remission probability), enabling personalized intervention decisions. All models demonstrated robust performance in predicting both treatment response and remission across validation cohorts. Their interpretability, clinical grounding, and consistent predictive accuracy support feasibility for real-world clinical deployment to guide evidence-based, individualized BDD management.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis, we employ multiple machine learning approaches to predict treatment outcomes -- specifically treatment response and remission -- with an emphasis on interpretability to ensure clinical relevance and utility. Across the various models investigated, treatment credibility emerged as the most potent predictor, surpassing traditional markers such as baseline symptom severity or comorbid conditions. Notably, while simpler models (e.g., logistic regression and support vector machines) achieved competitive predictive performance, decision tree analyses provided unique insights by revealing clinically interpretable threshold values in credibility scores. These thresholds can serve as practical guideposts for clinicians when tailoring interventions or allocating treatment resources. We further contextualize our findings within the broader literature on BDD, addressing technology-based therapeutics, digital interventions, and the psychosocial determinants of treatment engagement. An extensive array of references situates our results within current research on BDD prevalence, suicidality risks, and digital innovation. Our work underscores the potential of integrating rigorous statistical methodologies with transparent machine learning models. By systematically identifying modifiable predictors -- such as treatment credibility -- we propose a pathway toward more targeted, personalized, and ultimately efficacious interventions for individuals with BDD.

Problem

• Predict treatment response in Body Dysmorphic Disorder using interpretable machine learning.
• Identify treatment credibility as a key predictor for BDD outcomes.
• Provide clinically interpretable thresholds for tailoring BDD interventions.

Innovation

• Interpretable machine learning predicts BDD treatment outcomes.
• Treatment credibility identified as key predictive factor.
• Decision trees reveal clinically actionable credibility thresholds.