Does Synthetic Data Generation of LLMs Help Clinical Text Mining? (2303.04360v2)

Abstract: Recent advancements in 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.

Evaluating the Role of Synthetic Data Generation by LLMs in Enhancing Clinical Text Mining

The paper "Does Synthetic Data Generation of LLMs Help Clinical Text Mining?" by Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu provides a rigorous investigation into the utility of LLMs, specifically OpenAI's ChatGPT, in advancing clinical text mining tasks. This paper focuses on the capabilities of LLMs in addressing biomedical named entity recognition (NER) and relation extraction (RE) from unstructured healthcare data, highlighting both the potential benefits and inherent limitations.

Key Findings

Despite the substantial advancements of LLMs, initial attempts to directly apply ChatGPT to biomedical tasks resulted in suboptimal performance. The Named Entity Recognition task achieved an F1-score of 37.92% with ChatGPT, significantly lower than the 86.08% achieved by state-of-the-art (SOTA) models. Similarly, for Relation Extraction, ChatGPT produced an F1-score of 78.03%, compared to 88.96% by SOTA models. These results underscore the limitations of applying general-purpose LLMs, like ChatGPT, without task-specific training in specialized domains such as healthcare.

To bridge this performance gap, the authors propose a novel training paradigm using synthetic data generated by LLMs. The methodology involves generating large volumes of high-quality labeled synthetic data via ChatGPT, subsequently used for fine-tuning a local model. This approach significantly improved model performance, achieving an F1-score of 63.99% for the NER task and 83.69% for the RE task when fine-tuned on synthetic data. This demonstrates the potential of synthetic data generation in overcoming the domain-specific limitations of LLMs.

Implications

The implications of this paper are multifaceted:

1. Performance Enhancement: By employing synthetic data generation, local models can achieve performance levels comparable to SOTA models, alleviating the need for extensive domain-specific data labeling.
2. Privacy Concerns: Utilizing synthetic data mitigates privacy risks associated with uploading patient information to external APIs, allowing healthcare providers to maintain robust data privacy protocols.
3. Resource Efficiency: The generation of synthetic data reduces the time and effort required for data collection and labeling, facilitating agile model development processes.

These findings highlight a pragmatic application of LLM-driven synthetic data generation in enhancing the effectiveness of clinical text mining while addressing critical privacy considerations inherent in healthcare data handling.

Future Directions

The paper opens avenues for further research in several directions:

• Quality of Synthetic Data: Continued refinement of prompt strategies and post-processing techniques to ensure synthetic data closely mirrors the distribution and complexity of real-world data.
• Expansion to Additional Clinical Tasks: Investigating the applicability of synthetic data generation for other clinical text mining tasks apart from NER and RE.
• Integration of Domain Knowledge: Incorporating domain-specific knowledge into LLMs to improve zero-shot learning capacities, potentially reducing reliance on synthetic data.

The application of LLMs for synthetic data generation represents a compelling progression in clinical text mining technologies, catalyzing advancements in model performance, privacy protection, and data handling efficiencies. As models and methodologies continue to evolve, the integration of LLMs into healthcare tasks promises significant improvements in clinical data processing capabilities.