Abstract
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.
Abstract (translated)
主诉（CC）是指患者用自己的语言表达的就诊原因。它有助于医疗专业人员快速了解患者的状况，并且也作为医学文本挖掘的简短摘要使用。然而，由于主诉记录通常采用多种输入方法，导致医疗注释形式多样，这使得在不同的医疗机构之间难以标准化记录或进行文本挖掘。 在这项研究中，我们提出了一种无需人工标注即可自动提取和链接主诉中的实体的弱监督方法。首先，我们采用了分段匹配算法，在120万条经过去识别化且获得伦理委员会批准的真实世界主诉记录上生成弱标签，包括实体提及范围和类别标签。然后，我们使用基于BERT的模型和生成的弱标签来定位主诉文本中的实体，并将其链接到预定义的本体论中。 我们进行了广泛的实验，结果表明，在没有人工标注的情况下，我们的弱监督实体提取和链接（\ours）方法在性能上超过了以前的方法。
URL
https://arxiv.org/abs/2509.01899
