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AwareDX: Using Machine Learning to Identify Drugs Posing Increased Risk of Adverse Reactions to Women

24 Pages Posted: 30 Jun 2020 Publication Status: Review Complete

See all articles by Payal Chandak

Payal Chandak

Columbia University - Department of Computer Science

Nicholas P. Tatonetti

Columbia University - Department of Biomedical Informatics

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Abstract

Adverse drug reactions (ADRs) are the fourth leading cause of death in the US. Although women take longer to metabolize medications and experience twice the risk of developing ADRs compared to men, these sex differences are not comprehensively understood. Real-world clinical data provides an opportunity to estimate safety effects in otherwise understudied populations, ie. women. These data, however, are subject to confounding biases and correlated covariates. We present AwareDX, a pharmacovigilance algorithm that leverages advances in machine learning to study sex risks. Our algorithm mitigates these biases and quantifies the differential risk of a drug causing an adverse event in either men or women. We present a resource of 20,817 adverse drug effects posing sex specific risks. We independently validated our algorithm against known pharmacogenetic mechanisms of genes that are sex-differentially expressed. AwareDX presents an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.

Keywords: machine learning, data science, Bias and covariates, Sex, Gender, Women, Adverse reactions, Drugs, Pharmacogenetics, Pharmacovigilance

Suggested Citation

Chandak, Payal and Tatonetti, Nicholas P., AwareDX: Using Machine Learning to Identify Drugs Posing Increased Risk of Adverse Reactions to Women. Available at SSRN: https://ssrn.com/abstract=3624461 or http://dx.doi.org/10.2139/ssrn.3624461
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Payal Chandak (Contact Author)

Columbia University - Department of Computer Science ( email )

New York, NY 10027
United States

Nicholas P. Tatonetti

Columbia University - Department of Biomedical Informatics ( email )

622 W. 168th Street
Presbyterian Building 20th Floor
New York, NY 10032
United States

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