用语义框架分析病历文本，提升对性别暴力的识别能力。
Evaluating FrameNet-Based Semantic Modeling for Gender-Based Violence Detection in Clinical Records
- 基于FrameNet的语义标注捕捉临床文本中的暴力模式。
- 融合语义标注的模型F1得分提升超0.3，优于纯结构化数据。
- 适合医疗文本分析与公共健康干预研究者参考。
性别暴力（GBV）是重大公共卫生问题，世卫组织估计全球三分之一女性一生中会遭遇亲密伴侣的身心或性暴力。在巴西，尽管医护人员有法律义务报告此类事件，但因难以识别虐待行为及公共信息系统整合不足，仍存在显著漏报。本研究探讨电子病历中开放式文本的FrameNet语义标注是否有助于识别GBV模式。比较了三种SVM分类器性能：(1) 基于框架标注文本、(2) 标注文本与参数化数据结合、(3) 仅使用参数化数据。定量与定性分析表明，融入语义标注的模型优于仅依赖结构化数据的模型，F1分数提升超过0.3，证明领域特定语义表征能提供结构数据之外的有效信号。研究支持语义分析可增强早期识别策略，助力更精准的公共卫生干预。
原文摘要 · Abstract (English)
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.
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