A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data

15Citations
Citations of this article
126Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven’t been indicated for treating coronary artery disease, paving the way for drug repurposing.

Cite

CITATION STYLE

APA

Liu, R., Wei, L., & Zhang, P. (2021). A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nature Machine Intelligence, 3(1), 68–75. https://doi.org/10.1038/s42256-020-00276-w

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free