Matching patients to clinical trials demands a systematic and reasoned interpretation of documents which require significant expert-level background knowledge, over a complex set of well-defined eligibility criteria. Moreover, this interpretation process needs to operate at scale, over vast knowledge bases of trials. In this paper, we propose a scalable method that extends the capabilities of LLMs in the direction of systematizing the reasoning over sets of medical eligibility criteria, evaluating it in the context of real-world cases. The proposed method overlays a Set-guided reasoning method for LLMs. The proposed framework is evaluated on TREC 2022 Clinical Trials, achieving results superior to the state-of-the-art: NDCG@10 of 0.693 and Precision@10 of 0.73.
@article{arxiv.2409.18998,
title = {Controlled LLM-based Reasoning for Clinical Trial Retrieval},
author = {Mael Jullien and Alex Bogatu and Harriet Unsworth and Andre Freitas},
journal= {arXiv preprint arXiv:2409.18998},
year = {2024}
}