  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.
