Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification

To encourage advancements in data analytics on EHRs, The identification of key factors such as medications, diseases, and the N2C2 2022 competitions have invited teams to participate in relationships within electronic health records and clinical notes has various tasks aimed at identifying key factors such as medications, a wide range of applications in the clinical field. In the N2C2 2022 diseases, and relationships within the Contextualized Medication competitions, various tasks were presented to promote the identification Event Dataset (CMED) [10]. of key factors in electronic health records (EHRs) using Over the past few years, there has been a significant breakthrough the Contextualized Medication Event Dataset (CMED). Pretrained in natural language processing (NLP) tasks with the introduction large language models (LLMs) demonstrated exceptional performance of pretrained large language models (LLMs) such as BERT. in these tasks. This study aims to explore the utilization of These LLMs are transformer-based architectures that undergo unsupervised LLMs, specifically ChatGPT, for data augmentation to overcome training on extensive text data to comprehend the intricate the limited availability of annotated data for identifying the key features and patterns of human language.