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

TL;DR

The paper tackles data scarcity and privacy constraints in clinical trials by proposing a Retrieval-Reasoning few-shot framework that generates synthetic trials with binary outcomes anchored to real interventions from DrugBank. It leverages large language models to perform in-context reasoning and report generation, yielding 3358 synthetic trials, and demonstrates that hybrid fine-tuning of BioBERT on synthetic plus real data improves downstream trial-outcome prediction, outperforming single-source training. Analyses using t-SNE and cosine similarity indicate that synthetic data broadens distributional coverage and enhances model robustness, while visualizations reveal distinct yet complementary clusters between real and synthetic trials. The approach offers a privacy-preserving pathway to augment clinical datasets and accelerate research, with thoughtful discussion of limitations and potential risks in model bias and scope, and suggestions for future multimodal extensions.

Abstract

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.

Figures (3)

• Figure 1: The overall pipeline of retrieval-reasoning clinical trial generation.
• Figure 2: Distributions of cosine similarities between pairs of embeddings (a) within distributions (b) across distributions.
• Figure 3: t-SNE visualization of real vs synthetic trials.