Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
This work addresses data scarcity and privacy issues in clinical research, offering a method to accelerate studies while upholding ethical standards, though it is incremental as it builds on existing LLM capabilities.
The authors tackled the problem of data scarcity in clinical machine learning by introducing a Retrieval-Reasoning framework that uses large language models to generate synthetic clinical trials, demonstrating that this synthetic data can augment real datasets and improve model training for tasks like trial outcome prediction.
Machine learning (ML) exhibits promise in the clinical domain. However, it is 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 for conducting studies with human participants. Despite the advancements of large language models (LLMs) in general generation tasks, their potential in facilitating the generation of synthetic clinical trials is under-explored. 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. Experiments conducted on real clinical trials from the \url{ClinicalTrials.gov} database demonstrate that our 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 LLMs for synthetic clinical trial generation hold promise for accelerating clinical research and upholding ethical standards for patient privacy. The code is publicly available at https://anonymous.4open.science/r/Retrieval_Reasoning_Clinical_Trial_Generation-3EC4.