Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation

Authors: Zerui Xu, Fang Wu, Tianfan Fu, Yue Zhao
Primary area: foundation or frontier models, including LLMs
Keywords: Synthetic Data Generation, Large Language Model, Clinical NLP

Abstract:
Machine learning (ML) has exhibited considerable promise in the clinical domain. However, its capabilities are constrained by data scarcity and ethical considerations, as the generation of clinical trials presents significant challenges due to stringent privacy regulations, high costs, and the extended duration required required for conducting studies with human participants. Despite the advancements of large language models (LLMs) in natural language understanding and generation, limited research has explored their potential in facilitating the generation of synthetic clinical trials. To address this gap, we introduce a novel Retrieval-Reasoning few-shot framework that leverages LLMs to generate artificial yet realistic and diverse clinical trials with binary success/failure labels. Extensive experiments conducted on real clinical trials from the ClinicalTrials.gov database demonstrate that our generated synthetic data can effectively augment real datasets. Furthermore, by fine-tuning a pre-trained model as a binary classifier on synthetic clinical trial datasets, we demonstrate that this augmentation enhances model training for downstream tasks such as trial outcome prediction. Our findings suggest that leveraging LLMs for synthetic clinical trial generation holds significant promise for accelerating clinical research, enabling more effective ML models in healthcare, and upholding ethical standards for patient privacy.

Decision: reject

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