Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records

With the rapid growth of digital health technologies, electronic health records (EHRs) have become central to healthcare systems worldwide. EHRs capture longitudinal patient trajectories across demographics, diagnoses, laboratory tests, medications, physiological signals, and procedures, providing a robust foundation for early disease detection and risk prediction. Yet, the richness of these data also brings challenges: they are vast, complex, and heterogeneous, making traditional analytic approaches insufficient. Recent advances in machine learning (ML) and deep learning (DL) offer transformative solutions, with the ability to model high-dimensional information, uncover nonlinear latent patterns, and generate clinically actionable predictions. This chapter aims to provide a comprehensive overview of how ML and DL can be applied to EHRs for early disease detection and prevention. It highlights methodological advances, practical applications, and two real-world case studies, while addressing the challenges that must be overcome for safe and trustworthy clinical integration.