Automated Biomarker Discovery and Predictive Modeling for Drug-Induced Liver Injury (DILI) Using Multi-Modal Data Fusion and Advanced Machine Learning

**Abstract:** Drug-Induced Liver Injury (DILI) represents a significant clinical challenge with unpredictable onset and severity. Traditional diagnostic methods often lack sensitivity and specificity, hindering timely intervention. This paper presents a novel framework for automated biomarker discovery and predictive modeling for DILI utilizing a multi-modal data fusion approach combined with advanced machine learning algorithms. We demonstrate how integrating clinical chemistry, transcriptomic, proteomic, and electronic health record (EHR) data can significantly improve DILI prediction accuracy and identify previously unrecognized biomarkers, ultimately leading to improved patient outcomes and reduced healthcare costs. The implemented system exhibits superior performance compared to existing methods and establishes a foundation for proactive DILI risk stratification and personalized therapeutic interventions.