Controlled LLM-based Reasoning for Clinical Trial Retrieval

• URL: http://arxiv.org/abs/2409.18998v1
• Date: Thu, 19 Sep 2024 09:42:33 GMT
• Title: Controlled LLM-based Reasoning for Clinical Trial Retrieval
• Authors: Mael Jullien, Alex Bogatu, Harriet Unsworth, Andre Freitas,
• Abstract summary: We propose a scalable method that extends the capabilities of LLMs in the direction of systematizing the reasoning over sets of medical eligibility criteria. The proposed method 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.
• Score: 0.4199844472131922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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