Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records
This work addresses the challenge of identifying GBV cases in clinical settings to support public health interventions, though it is incremental as it applies an existing method to a new domain.
This study tackled the problem of underreporting gender-based violence (GBV) in Brazil by investigating FrameNet-based semantic annotation of clinical records to detect GBV patterns, finding that models using semantic annotation improved F1 scores by over 0.3 compared to categorical models.
Gender-based violence (GBV) is a major public health issue, with the World Health Organization estimating that one in three women experiences physical or sexual violence by an intimate partner during her lifetime. In Brazil, although healthcare professionals are legally required to report such cases, underreporting remains significant due to difficulties in identifying abuse and limited integration between public information systems. This study investigates whether FrameNet-based semantic annotation of open-text fields in electronic medical records can support the identification of patterns of GBV. We compare the performance of an SVM classifier for GBV cases trained on (1) frame-annotated text, (2) annotated text combined with parameterized data, and (3) parameterized data alone. Quantitative and qualitative analyses show that models incorporating semantic annotation outperform categorical models, achieving over 0.3 improvement in F1 score and demonstrating that domain-specific semantic representations provide meaningful signals beyond structured demographic data. The findings support the hypothesis that semantic analysis of clinical narratives can enhance early identification strategies and support more informed public health interventions.