AI-Based Clinical Endpoint Prediction

Abstract

Clinical endpoint prediction -- forecasting whether a patient will achieve a therapeutic response, experience disease progression, or develop complications within a defined time horizon -- is the foundational capability for precision medicine, adaptive trial design, and value-based healthcare delivery. Traditional prognostic models use Cox proportional hazards or logistic regression with manually selected clinical variables, achieving moderate discrimination (C-statistic 0.65-0.75) that limits their utility for individual patient decision-making. AI-based approaches leverage high-dimensional data, non-linear interactions, and temporal patterns to substantially improve prediction performance. This study presents the AI Clinical Endpoint Prediction Framework (AICEPF), evaluating five prediction approaches -- traditional statistical models, gradient-boosted machine learning, deep learning with temporal attention, multi-modal transformer integration, and federated learning across institutional boundaries -- across four endpoint categories: treatment response prediction, disease progression forecasting, adverse outcome risk stratification, and composite endpoint modelling. Our Clinical Endpoint Prediction Score (CEPS) integrates discriminative accuracy, calibration quality, temporal precision, clinical utility, and generalisability. Multi-modal transformer integration achieved the highest CEPS (0.928) through cross-attention mechanisms fusing laboratory trajectories, imaging features, genomic markers, and clinical notes that achieved C-statistic 0.89 for 12-month composite endpoint prediction, while federated learning achieved the highest generalisability (0.955) through privacy-preserving multi-institutional training that maintained 96% of centralised model performance across 8 demographically diverse hospital systems