Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women
Summary: 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 pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Patterns |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666389920301422 |
| _version_ | 1818212265820684288 |
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| author | Payal Chandak Nicholas P. Tatonetti |
| author_facet | Payal Chandak Nicholas P. Tatonetti |
| author_sort | Payal Chandak |
| collection | DOAJ |
| description | Summary: 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. The Bigger Picture: We present the first, and to our knowledge only, approach for predicting sex differences in drug response corroborated by pharmacogenomic data. Our algorithm AwareDX identifies drugs associated with increased rates of adverse events to either sex. AwareDX uses machine learning to dampen correlated covariates and mitigate confounding biases of sex. This approach has the potential to generalize to understudied populations in many data science domains. We introduce a resource of sex-specific adverse drug effects for use in drug discovery, repositioning, and pharmacogenetic studies and for further analysis through electronic health records and clinical trials. Ultimately, such analyses could potentially raise awareness of sex differences during clinical decision making. |
| first_indexed | 2024-12-12T05:45:39Z |
| format | Article |
| id | doaj.art-2aca26cd37484f509614413c780a328e |
| institution | Directory Open Access Journal |
| issn | 2666-3899 |
| language | English |
| last_indexed | 2024-12-12T05:45:39Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Patterns |
| spelling | doaj.art-2aca26cd37484f509614413c780a328e2022-12-22T00:35:48ZengElsevierPatterns2666-38992020-10-0117100108Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to WomenPayal Chandak0Nicholas P. Tatonetti1Department of Computer Science, Columbia University, New York, NY 10027, USADepartment of Biomedical Informatics, Columbia University, New York, NY 10027, USA; Corresponding authorSummary: 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. The Bigger Picture: We present the first, and to our knowledge only, approach for predicting sex differences in drug response corroborated by pharmacogenomic data. Our algorithm AwareDX identifies drugs associated with increased rates of adverse events to either sex. AwareDX uses machine learning to dampen correlated covariates and mitigate confounding biases of sex. This approach has the potential to generalize to understudied populations in many data science domains. We introduce a resource of sex-specific adverse drug effects for use in drug discovery, repositioning, and pharmacogenetic studies and for further analysis through electronic health records and clinical trials. Ultimately, such analyses could potentially raise awareness of sex differences during clinical decision making.http://www.sciencedirect.com/science/article/pii/S2666389920301422machine learningdata scienceadverse drug reactionssexgenderwomen |
| spellingShingle | Payal Chandak Nicholas P. Tatonetti Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women Patterns machine learning data science adverse drug reactions sex gender women |
| title | Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women |
| title_full | Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women |
| title_fullStr | Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women |
| title_full_unstemmed | Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women |
| title_short | Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women |
| title_sort | using machine learning to identify adverse drug effects posing increased risk to women |
| topic | machine learning data science adverse drug reactions sex gender women |
| url | http://www.sciencedirect.com/science/article/pii/S2666389920301422 |
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