Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

TL;DR

This paper explores using synthetic data generated by ChatGPT to improve clinical text mining, addressing privacy issues and enhancing model performance in extracting biological entities and relations from healthcare texts.

Contribution

The study introduces a novel training paradigm that leverages ChatGPT to generate labeled synthetic data for fine-tuning local models in clinical text mining tasks.

Findings

F1-score for named entity recognition improved from 23.37% to 63.99%.

F1-score for relation extraction increased from 75.86% to 83.59%.

Synthetic data generation reduces data collection time and privacy concerns.

Abstract

Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. However, our preliminary results indicate that employing ChatGPT directly for these tasks resulted in poor performance and raised privacy concerns associated with uploading patients' information to the ChatGPT API. To overcome these limitations, we propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data with labels utilizing ChatGPT and fine-tuning a local model for the downstream task. Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23.37% to 63.99% for the named entity recognition task and from 75.86% to 83.59% for the relation extraction task. Furthermore, generating data using ChatGPT can significantly reduce the time and effort required for data collection and labeling, as well as mitigate data privacy concerns. In summary, the proposed framework presents a promising solution to enhance the applicability of LLM models to clinical text mining.