電子カルテとアソシエーションルールマイニングを用いた親密なパートナーからの暴力の潜在的パターンの特定

Intimate partner violence (IPV) remains a significant public health concern, yet its identification in emergency department (ED) settings is often challenging, as IPV-related ICD-10 are infrequently and inconsistently used. This study employs electronic health record (EHR) data and association rule mining to identify latent patterns indicative of IPV among female patients in ED. We applied the Apriori algorithm to identify associations between co-occurring health conditions in confirmed IPV survivors and non-IPV patients. The analysis revealed distinct patterns in both groups, with IPV survivors showing stronger links between physical injuries, mental health conditions (e.g., depression, suicidal ideation), and socioeconomic stressors. Non-IPV cases were primarily associated with anxiety disorders and family-related stressors. These findings highlight the potential of informatics-driven approaches to improve IPV detection by revealing subtle clinical signals that may notbe overtly disclosed. Our work paves the way for developing data-driven tools to enhance IPV screening and clinical decision-making in ED.