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This study explores the use of FrameNet-based semantic annotation to improve the detection of gender-based violence (GBV) in Brazilian electronic medical records. An SVM classifier was trained on frame-annotated text, frame-annotated text combined with parameterized data, and parameterized data alone. Results show that models incorporating semantic annotation outperform those using only categorical data, achieving a >0.3 F1 score improvement, suggesting that semantic representations capture meaningful signals beyond structured data.
FrameNet-based semantic annotation unlocks a 30% F1 score boost in detecting gender-based violence from clinical records, outperforming models relying solely on structured data.
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.
