A Scalable AI Approach for Clinical Trial Cohort Optimization

• URL: http://arxiv.org/abs/2109.02808v1
• Date: Tue, 7 Sep 2021 01:49:05 GMT
• Title: A Scalable AI Approach for Clinical Trial Cohort Optimization
• Authors: Xiong Liu, Cheng Shi, Uday Deore, Yingbo Wang, Myah Tran, Iya Khalil, Murthy Devarakonda
• Abstract summary: FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.
• Score: 6.076017404694899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.

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