用语义框架识别医疗记录中被漏报的性别暴力事件
Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence
- 构建8个语义模式，从2100万条葡萄牙语医疗文本中搜寻线索
- 识别准确率达0.726，有效发现被遗漏的暴力病例
- 方法透明可复用，适合公共卫生系统中的可解释性NLP应用
我们提出一种在医疗领域识别应报告事件的方法，利用语义框架定义细粒度模式，并在非结构化数据（如电子病历中的开放式文本字段）中进行搜索。该方法应用于初级保健机构中性别暴力（GBV）病例的漏报问题，基于巴西电子SUS APS系统的2100万句葡萄牙语文本语料库，定义并检索8种语义模式。结果经语言学家人工评估，各模式精度均被测量。研究发现，该方法能以0.726的精度有效识别暴力报告，证实其稳健性。该方法设计为透明、高效、低碳且语言无关，可轻松适配其他健康监测场景，推动公共健康系统中NLP技术的伦理与可解释应用。
原文摘要 · Abstract (English)
We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.
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