Bibliographic Information: Xu, Z., Wu, F., Fu, T., & Zhao, Y. (2024). Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation. arXiv preprint arXiv:2410.12476.
Research Objective: This paper aims to address the challenge of data scarcity in clinical trial research by developing a novel framework for generating synthetic clinical trials using large language models (LLMs).
Methodology: The researchers propose a Retrieval-Reasoning Few-shot Generation framework that utilizes an LLM (ChatGPT-4o-mini) to generate synthetic clinical trials. The framework consists of three modules:
Key Findings:
Main Conclusions:
Significance: This research significantly contributes to the field of machine learning in healthcare by providing a practical solution for generating synthetic clinical trial data. This approach has the potential to accelerate clinical research, reduce costs, and improve the efficiency of clinical trial design and analysis.
Limitations and Future Research:
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