Clinical trial matching is the task of identifying trials for which patients
may be potentially eligible. Typically, this task is labor-intensive and
requires detailed verification of patient electronic health records (EHRs)
against the stringent inclusion and exclusion criteria of clinical trials. This
process is manual, time-intensive, and challenging to scale up, resulting in
many patients missing out on potential therapeutic options. Recent advancements
in Large Language Models (LLMs) have made automating patient-trial matching
possible, as shown in multiple concurrent research studies. However, the
current approaches are confined to constrained, often synthetic datasets that
do not adequately mirror the complexities encountered in real-world medical
data. In this study, we present the first, end-to-end large-scale empirical
evaluation of clinical trial matching using real-world EHRs. Our study
showcases the capability of LLMs to accurately match patients with appropriate
clinical trials. We perform experiments with proprietary LLMs, including GPT-4
and GPT-3.5, as well as our custom fine-tuned model called OncoLLM and show
that OncoLLM, despite its significantly smaller size, not only outperforms
GPT-3.5 but also matches the performance of qualified medical doctors. All
experiments were carried out on real-world EHRs that include clinical notes and
available clinical trials from a single cancer center in the United States.

利用实际世界的电子健康记录，我们进行了第一次端到端的大规模实证评估，展示了大型语言模型在准确匹配患者和适当临床试验方面的能力。

PRISM: 利用大型语言模型进行语义临床试验匹配的患者病历解读

PRISM: Patient Records Interpretation for Semantic Clinical Trial  Matching using Large Language Models

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（LLMs）进行临床试验匹配在扩展规模中取得了积极的初步结果，特别在结构化患者信息和筛选患者 - 试验候选人方面起到了初步的解决方案作用。