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        <title>AutoTrial: Prompting Language Models for Clinical Trial Design</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T AutoTrial: Prompting Language Models for Clinical Trial Design
%A Wang, Zifeng
%A Xiao, Cao
%A Sun, Jimeng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-autotrial
%X 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.
%R 10.18653/v1/2023.emnlp-main.766
%U https://aclanthology.org/2023.emnlp-main.766
%U https://doi.org/10.18653/v1/2023.emnlp-main.766
%P 12461-12472

Markdown (Informal)

[AutoTrial: Prompting Language Models for Clinical Trial Design](https://aclanthology.org/2023.emnlp-main.766) (Wang et al., EMNLP 2023)

• AutoTrial: Prompting Language Models for Clinical Trial Design (Wang et al., EMNLP 2023)

ACL

• Zifeng Wang, Cao Xiao, and Jimeng Sun. 2023. AutoTrial: Prompting Language Models for Clinical Trial Design. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12461–12472, Singapore. Association for Computational Linguistics.