无需人工标注，自动提取并关联主诉中的医学实体
Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints
- 用拆分匹配算法生成120万条弱标注数据
- 基于BERT的模型在无标注下实现优异实体识别与链接
- 适合医疗文本挖掘与跨机构标准化场景
主诉是患者自述的就诊原因，有助于快速了解病情，也是医学文本挖掘的重要摘要。但主诉记录因录入方式多样，导致医学术语表达差异大，难以在不同医疗机构间统一标准。本研究提出一种弱监督方法，在无人工标注条件下自动提取并链接主诉中的医学实体。首先采用拆分匹配算法，在120万条真实、去标识化且经伦理审批的主诉记录上生成弱标注（包括实体提及跨度和类别标签）。随后，利用生成的弱标签训练基于BERT的模型，定位主诉文本中的实体并将其链接至预定义本体。大量实验表明，该弱监督实体抽取与链接方法（ extsc{OurS}）在无任何人工标注的情况下，性能优于以往方法。
原文摘要 · Abstract (English)
A Chief complaint (CC) is the reason for the medical visit as stated in the patient's own words. It helps medical professionals to quickly understand a patient's situation, and also serves as a short summary for medical text mining. However, chief complaint records often take a variety of entering methods, resulting in a wide variation of medical notations, which makes it difficult to standardize across different medical institutions for record keeping or text mining. In this study, we propose a weakly supervised method to automatically extract and link entities in chief complaints in the absence of human annotation. We first adopt a split-and-match algorithm to produce weak annotations, including entity mention spans and class labels, on 1.2 million real-world de-identified and IRB approved chief complaint records. Then we train a BERT-based model with generated weak labels to locate entity mentions in chief complaint text and link them to a pre-defined ontology. We conducted extensive experiments, and the results showed that our Weakly Supervised Entity Extraction and Linking (\ours) method produced superior performance over previous methods without any human annotation.
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