Mitigating bias in machine learning for medicine.
Mitigating bias in machine learning for medicine.
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DOI：
10.1038/s43856-021-00028-w
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发表时间：
2021-08-23
期刊：
影响因子：
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通讯作者：
Kesselheim AS
中科院分区：
文献类型：
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作者：
Vokinger KN;Feuerriegel S;Kesselheim AS
有几种偏差来源会影响医学中使用的机器学习系统的性能，并可能影响临床护理。在这里，我们讨论了减轻医疗应用中基于机器学习的系统的不同开发步骤中的偏见的解决方案。Vokinger等人讨论了医学中使用的机器学习系统的潜在偏差来源。作者提出了解决方案，以减轻模型开发的不同阶段的偏见，从数据收集和准备到模型评估和应用。
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications. Vokinger et al. discuss potential sources of bias in machine learning systems used in medicine. The authors propose solutions to mitigate bias across the different stages of model development, from data collection and preparation to model evaluation and application.