利用大型語言模型擴展臨床試驗配對：腫瘤學案例研究

摘要

臨床試驗配對是健康交付和發現的關鍵過程。在實踐中，它受到龐大的非結構化數據和不可擴展的手動處理的困擾。本文通過使用大型語言模型（LLMs）對臨床試驗配對進行系統性研究，以腫瘤學作為焦點領域。我們的研究基於目前正在美國一家大型醫療網絡中進行測試部署的臨床試驗配對系統。初步研究結果令人鼓舞：像GPT-4這樣的最新LLMs可以自動結構化臨床試驗的詳盡資格標準，並提取複雜的配對邏輯（例如，嵌套的AND/OR/NOT）。儘管仍遠非完美，LLMs明顯優於先前的強基線，可能作為幫助將患者-試驗候選人進行分類的初步解決方案。我們的研究還揭示了幾個應用LLMs進行端到端臨床試驗配對的重要增長領域，例如上下文限制和準確性，特別是在從長期醫療記錄中結構化患者信息方面。

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.

利用大型語言模型擴展臨床試驗配對：腫瘤學案例研究

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

摘要

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