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arXiv · 2509.01899

Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints

Zhimeng Luo·Zhendong Wang·Rui Meng·Diyang Xue·Adam Frisch·Daqing He
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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.

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Zhimeng Luo, Zhendong Wang, Rui Meng, Diyang Xue, Adam Frisch, Daqing He. 2025-09-02. Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints. https://arxiv.org/abs/2509.01899

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