Matching patients to clinical trials is a key unsolved challenge in bringing
new drugs to market. Today, identifying patients who meet a trial's eligibility
criteria is highly manual, taking up to 1 hour per patient. Automated screening
is challenging, however, as it requires understanding unstructured clinical
text. Large language models (LLMs) offer a promising solution. In this work, we
explore their application to trial matching. First, we design an LLM-based
system which, given a patient's medical history as unstructured clinical text,
evaluates whether that patient meets a set of inclusion criteria (also
specified as free text). Our zero-shot system achieves state-of-the-art scores
on the n2c2 2018 cohort selection benchmark. Second, we improve the data and
cost efficiency of our method by identifying a prompting strategy which matches
patients an order of magnitude faster and more cheaply than the status quo, and
develop a two-stage retrieval pipeline that reduces the number of tokens
processed by up to a third while retaining high performance. Third, we evaluate
the interpretability of our system by having clinicians evaluate the natural
language justifications generated by the LLM for each eligibility decision, and
show that it can output coherent explanations for 97% of its correct decisions
and 75% of its incorrect ones. Our results establish the feasibility of using
LLMs to accelerate clinical trial operations.

利用大型语言模型加速临床试验操作，设计了一个基于零 - shot 方法的系统来评估患者是否符合试验纳入标准，并通过优化数据和成本效率以及提高解释性来改进该方法。