Controlled LLM-based Reasoning for Clinical Trial Retrieval
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916413549051904 |
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| author | Jullien, Mael Bogatu, Alex Unsworth, Harriet Freitas, Andre |
| author_facet | Jullien, Mael Bogatu, Alex Unsworth, Harriet Freitas, Andre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controlled LLM-based Reasoning for Clinical Trial Retrieval Jullien, Mael Bogatu, Alex Unsworth, Harriet Freitas, Andre Computation and Language Artificial Intelligence 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. |
| title | Controlled LLM-based Reasoning for Clinical Trial Retrieval |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18998 |