A Scalable AI Approach for Clinical Trial Cohort Optimization

Autor:	Liu, Xiong, Shi, Cheng, Deore, Uday, Wang, Yingbo, Tran, Myah, Khalil, Iya, Devarakonda, Murthy
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Machine Learning Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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. Comment: 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)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.02808 Zobrazit plný text záznamu View this record from Arxiv