Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records
arXiv:2603.18124v1 Announce Type: new Abstract: 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 sc
arXiv:2603.18124v1 Announce Type: new Abstract: 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.
Executive Summary
This study explores the application of FrameNet-based semantic modeling in detecting gender-based violence (GBV) in clinical records. By leveraging semantic annotation of open-text fields, the research demonstrates improved performance in identifying GBV patterns compared to traditional categorical models. The findings suggest that incorporating semantic analysis of clinical narratives can enhance early identification strategies and inform public health interventions. The study's results have significant implications for healthcare professionals, policymakers, and public health stakeholders.
Key Points
- ▸ The study employs FrameNet-based semantic modeling to analyze clinical records for GBV detection.
- ▸ Semantic annotation outperforms categorical models in detecting GBV patterns.
- ▸ The research highlights the importance of integrating domain-specific semantic representations in identifying GBV.
Merits
Strength in Methodology
The study's use of FrameNet-based semantic modeling and quantitative analysis provides a robust framework for evaluating GBV detection in clinical records.
Significant Improvement in Detection Rates
The findings demonstrate a substantial improvement in GBV detection rates, showcasing the potential of semantic analysis in supporting early identification strategies.
Demerits
Limited Generalizability
The study's findings may not be generalizable to other healthcare settings or populations, highlighting the need for further research and validation.
Dependence on High-Quality Data
The effectiveness of semantic analysis relies on high-quality clinical data, which may not be consistently available or accurately recorded in all healthcare settings.
Expert Commentary
The study's findings have significant implications for the application of semantic analysis in healthcare, particularly in the context of GBV detection. While the research demonstrates the potential of FrameNet-based semantic modeling, it is essential to acknowledge the limitations and challenges associated with this approach. Future studies should prioritize addressing these limitations, exploring the generalizability of the findings, and investigating the feasibility of implementing semantic analysis in real-world healthcare settings. By doing so, researchers can further refine and validate the effectiveness of semantic analysis in supporting early identification strategies and improving patient outcomes.
Recommendations
- ✓ Future studies should prioritize the development of high-quality, standardized clinical data to support the application of semantic analysis in healthcare.
- ✓ Researchers should explore the integration of semantic analysis with other machine learning techniques to enhance GBV detection and prevention efforts.
