Clinical trials are critical for drug development. Constructing the
appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for
patient recruitment) is essential for the trial's success. Proper design of
clinical trial protocols should consider similar precedent trials and their
eligibility criteria to ensure sufficient patient coverage. In this paper, we
present a method named AutoTrial to aid the design of clinical eligibility
criteria using language models. It allows (1) controllable generation under
instructions via a hybrid of discrete and neural prompting, (2) scalable
knowledge incorporation via in-context learning, and (3) explicit reasoning
chains to provide rationales for understanding the outputs. Experiments on over
70K clinical trials verify that AutoTrial generates high-quality criteria texts
that are fluent and coherent and with high accuracy in capturing the relevant
clinical concepts to the target trial. It is noteworthy that our method, with a
much smaller parameter size, gains around 60\% winning rate against the GPT-3.5
baselines via human evaluations.

本文介绍了一种名为 AutoTrial 的方法，它使用语言模型帮助设计临床试验的甄别标准，可以在指令控制下进行可控生成，可以通过上下文学习进行可扩展的知识融合，提供明确的推理链以理解输出，通过人类评估与 GPT-3.5 基线获得了高质量的标准文本和 60％的胜率。