Uncovering Hidden Patterns

Association Rule Mining (ARM) has become an important tool for uncovering hidden patterns and clinically meaningful relationships in large healthcare datasets. With the growth of electronic health records, administrative claims, and patient monitoring systems, ARM helps identify disease co-occurrences, multimorbidity patterns, medication risks, and factors influencing patient outcomes. This review examines key ARM algorithms, recent methodological advances, and their applications in diagnosis support, drug safety, and precision medicine. It also highlights ongoing challenges related to data quality, integration, and clinical interpretability, while outlining future opportunities for improving patient management.