Summary of Controlled Llm-based Reasoning For Clinical Trial Retrieval, by Mael Jullien and Alex Bogatu and Harriet Unsworth and Andre Freitas
Controlled LLM-based Reasoning for Clinical Trial Retrieval
by Mael Jullien, Alex Bogatu, Harriet Unsworth, Andre Freitas
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a scalable method for matching patients to clinical trials by systematizing reasoning over sets of medical eligibility criteria using Large Language Models (LLMs). The approach, which extends the capabilities of LLMs, is evaluated on the TREC 2022 Clinical Trials dataset and achieves state-of-the-art results with an NDCG@10 score of 0.693 and Precision@10 score of 0.73. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to help doctors match patients with clinical trials that fit their needs. They used special computer models called Large Language Models (LLMs) to analyze lots of medical information and make smart decisions. The team tested their approach on real-world data and got better results than others who tried the same thing before. |
Keywords
» Artificial intelligence » Precision
