Clinical Trial Participant Retention Prediction Model

Design a sophisticated Python-based predictive model to forecast clinical trial participant dropout probabilities using advanced machine learning techniques. Integrate multiple data sources including demographic information, medical history, trial protocol complexity, and historical dropout patterns. Implement ensemble learning methods (Random Forest, Gradient Boosting) with comprehensive feature engineering and cross-validation. Generate an interpretable risk assessment dashboard with actionable insights for clinical research teams.