Algorithmic fairness in computational medicine
Summary: Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
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| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396422004327 |
| _version_ | 1798036700880961536 |
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| author | Jie Xu Yunyu Xiao Wendy Hui Wang Yue Ning Elizabeth A. Shenkman Jiang Bian Fei Wang |
| author_facet | Jie Xu Yunyu Xiao Wendy Hui Wang Yue Ning Elizabeth A. Shenkman Jiang Bian Fei Wang |
| author_sort | Jie Xu |
| collection | DOAJ |
| description | Summary: Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of specific demographic groups such as vulnerable ethnic minorities. This problem, termed algorithmic bias, has been extensively studied in theoretical machine learning recently. However, the impact of algorithmic bias on medicine and methods to mitigate this bias remain topics of active discussion. This paper presents a comprehensive review of algorithmic fairness in the context of computational medicine, which aims at improving medicine with computational approaches. Specifically, we overview the different types of algorithmic bias, fairness quantification metrics, and bias mitigation methods, and summarize popular software libraries and tools for bias evaluation and mitigation, with the goal of providing reference and insights to researchers and practitioners in computational medicine. |
| first_indexed | 2024-04-11T21:16:34Z |
| format | Article |
| id | doaj.art-432d6720db7148d2afaf33d6d7469cd4 |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-04-11T21:16:34Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-432d6720db7148d2afaf33d6d7469cd42022-12-22T04:02:47ZengElsevierEBioMedicine2352-39642022-10-0184104250Algorithmic fairness in computational medicineJie Xu0Yunyu Xiao1Wendy Hui Wang2Yue Ning3Elizabeth A. Shenkman4Jiang Bian5Fei Wang6Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USADepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USADepartment of Computer Science, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Computer Science, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USADepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USADepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA; Corresponding author.Summary: Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of specific demographic groups such as vulnerable ethnic minorities. This problem, termed algorithmic bias, has been extensively studied in theoretical machine learning recently. However, the impact of algorithmic bias on medicine and methods to mitigate this bias remain topics of active discussion. This paper presents a comprehensive review of algorithmic fairness in the context of computational medicine, which aims at improving medicine with computational approaches. Specifically, we overview the different types of algorithmic bias, fairness quantification metrics, and bias mitigation methods, and summarize popular software libraries and tools for bias evaluation and mitigation, with the goal of providing reference and insights to researchers and practitioners in computational medicine.http://www.sciencedirect.com/science/article/pii/S2352396422004327Algorithmic fairnessComputational medicine |
| spellingShingle | Jie Xu Yunyu Xiao Wendy Hui Wang Yue Ning Elizabeth A. Shenkman Jiang Bian Fei Wang Algorithmic fairness in computational medicine EBioMedicine Algorithmic fairness Computational medicine |
| title | Algorithmic fairness in computational medicine |
| title_full | Algorithmic fairness in computational medicine |
| title_fullStr | Algorithmic fairness in computational medicine |
| title_full_unstemmed | Algorithmic fairness in computational medicine |
| title_short | Algorithmic fairness in computational medicine |
| title_sort | algorithmic fairness in computational medicine |
| topic | Algorithmic fairness Computational medicine |
| url | http://www.sciencedirect.com/science/article/pii/S2352396422004327 |
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