AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
Clinical trials are critical for advancing medical treatments but remain
prohibitively expensive and time-consuming. Accurate prediction of clinical
trial outcomes can significantly reduce research and development costs and
accelerate drug discovery. While recent deep learning models have shown promise
by leveraging unstructured data, their black-box nature, lack of
interpretability, and vulnerability to label leakage limit their practical use
in high-stakes biomedical contexts. In this work, we propose AutoCT, a novel
framework that combines the reasoning capabilities of large language models
with the explainability of classical machine learning. AutoCT autonomously
generates, evaluates, and refines tabular features based on public information
without human input. Our method uses Monte Carlo Tree Search to iteratively
optimize predictive performance. Experimental results show that AutoCT performs
on par with or better than SOTA methods on clinical trial prediction tasks
within only a limited number of self-refinement iterations, establishing a new
paradigm for scalable, interpretable, and cost-efficient clinical trial
prediction.
