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Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women
Payal Chandak1, Nicholas P Tatonetti2,3
1Department of Computer Science, Columbia University, New York, NY 10027, USA.
Abstract:
Adverse drug reactions are the fourth leading cause of death in the US. Although women take longer to metabolize medications and experience twice the risk of developing adverse reactions compared with men, these sex differences are not comprehensively understood. Real-world clinical data provide an opportunity to estimate safety effects in otherwise understudied populations, i.e., 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 predict sex risks. Our algorithm mitigates these biases and quantifies the differential risk of a drug causing an adverse event in either men or women. AwareDX demonstrates high precision during validation against clinical literature and pharmacogenetic mechanisms. We present a resource of 20,817 adverse drug effects posing sex-specific risks. AwareDX, and this resource, present an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.
Insights
Adverse drug reactions disproportionately affect women. A new machine learning algorithm, AwareDX, identifies sex-specific drug risks, offering a resource to personalize medication for improved safety.
Area of Science:
- Pharmacovigilance
- Machine Learning
- Drug Safety
Background:
- Adverse drug reactions (ADRs) are a significant cause of mortality.
- Women experience twice the risk of ADRs compared to men, yet these differences are poorly understood.
- Real-world clinical data offer insights but are prone to confounding biases.
Purpose of the Study:
- To develop and validate a machine learning algorithm (AwareDX) to predict sex-specific drug risks.
- To quantify differential risks of adverse events between men and women.
- To create a resource identifying drugs with sex-specific risks.
Main Methods:
- Utilized a pharmacovigilance algorithm (AwareDX) incorporating machine learning.
- Mitigated confounding biases and correlated covariates in real-world clinical data.
- Validated the algorithm against clinical literature and pharmacogenetic mechanisms.
Main Results:
- Identified 20,817 adverse drug effects with sex-specific risks.
- Demonstrated high precision in predicting differential drug risks between sexes.
- Quantified the varying likelihood of adverse events based on sex.
Conclusions:
- AwareDX effectively predicts sex-specific drug risks, addressing a critical gap in pharmacovigilance.
- The developed resource aids in understanding and mitigating ADRs in understudied populations.
- Personalizing drug prescription and dosage based on sex can minimize adverse events.
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