A Scalable AI Approach for Clinical Trial Cohort Optimization

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Sep 07, 2021
Xiong Liu, Cheng Shi, Uday Deore, Yingbo Wang, Myah Tran, Iya Khalil, Murthy Devarakonda

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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.

* PharML 2021 (Machine Learning for Pharma and Healthcare Applications) at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)  
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