Algorithmic fairness in computational medicine.

Algorithmic fairness in computational medicine.
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计算医学中的数学公平性。

DOI:
10.1016/j.ebiom.2022.104250
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发表时间:
2022-10
期刊:
影响因子:
11.1
通讯作者:
Wang, Fei
Wang, Fei
中科院分区:
医学1区
文献类型:
--
作者:
Xu, Jie;Xiao, Yunyu;Wang, Wendy Hui;Ning, Yue;Shenkman, Elizabeth A.;Bian, Jiang;Wang, Fei

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机器学习模型越来越多地被用于促进临床决策。然而,最近的研究表明,机器学习技术在为不同子群体的人做出决策时可能会导致潜在的偏见,这可能会对特定人口群体(如弱势少数民族)的健康和福祉产生不利影响。这个问题被称为算法偏差,最近在理论机器学习中得到了广泛的研究。然而,算法偏差对医学的影响以及减轻这种偏差的方法仍然是积极讨论的话题。本文提出了一个全面的审查算法的公平性计算医学的背景下,其目的是提高医学与计算方法。具体而言,我们概述了不同类型的算法偏差,公平性量化指标和偏差缓解方法,并总结了流行的软件库和工具的偏差评估和缓解,其目标是提供参考和见解的研究人员和从业人员在计算医学。
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.
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