Controlled LLM-based Reasoning for Clinical Trial Retrieval
<|reference_start|>Controlled LLM-based Reasoning for Clinical Trial Retrieval: 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.<|reference_end|>
@article{jullien2024controlled,
    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},
    archivePrefix={arXiv},
    eprint={2409.18998},
    primaryClass={cs.CL cs.AI}
}