The identification of key factors such as medications, diseases, and
relationships within electronic health records and clinical notes has a wide
range of applications in the clinical field. In the N2C2 2022 competitions,
various tasks were presented to promote the identification of key factors in
electronic health records (EHRs) using the Contextualized Medication Event
Dataset (CMED). Pretrained large language models (LLMs) demonstrated
exceptional performance in these tasks. This study aims to explore the
utilization of LLMs, specifically ChatGPT, for data augmentation to overcome
the limited availability of annotated data for identifying the key factors in
EHRs. Additionally, different pre-trained BERT models, initially trained on
extensive datasets like Wikipedia and MIMIC, were employed to develop models
for identifying these key variables in EHRs through fine-tuning on augmented
datasets. The experimental results of two EHR analysis tasks, namely medication
identification and medication event classification, indicate that data
augmentation based on ChatGPT proves beneficial in improving performance for
both medication identification and medication event classification.

本研究旨在探讨利用预训练的大型语言模型 (ChatGPT) 进行数据增强来识别电子病历中的关键因素，结果表明基于 ChatGPT 的数据增强对药物识别和药物事件分类任务的性能提高有益。