利用大型语言模型扩展临床试验匹配：肿瘤学案例研究

摘要

临床试验匹配是卫生交付和发现中的关键过程。在实践中，它受到庞大的非结构化数据和不可扩展的手动处理的困扰。本文通过使用大型语言模型（LLMs）对临床试验匹配进行规模化研究，以肿瘤学作为重点领域。我们的研究基于目前正在美国一家大型医疗网络进行测试部署的临床试验匹配系统。初步发现是令人鼓舞的：像GPT-4这样的最新LLMs可以已经结构化临床试验的复杂资格标准，并提取复杂的匹配逻辑（例如，嵌套的AND/OR/NOT）。虽然仍然远非完美，LLMs明显优于先前的强基线，并可能作为帮助在人类参与的情况下筛选患者-试验候选者的初步解决方案。我们的研究还揭示了将LLMs应用于端到端临床试验匹配的一些重要增长领域，例如上下文限制和准确性，特别是在从纵向医疗记录中构建患者信息方面。

English

Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network. Initial findings are promising: out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.

利用大型语言模型扩展临床试验匹配：肿瘤学案例研究

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology

摘要

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