Medical Data Augmentation via ChatGPT: A Case Study on Medication
  Identification and Medication Event Classification
Shouvon Sarker Lijun Qian Xishuang Dong
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.