Vokinger Kerstin N, Feuerriegel Stefan, Kesselheim Aaron S
Institute of Law, University of Zurich, Zurich, Switzerland.
Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Commun Med (Lond). 2021 Aug 23;1:25. doi: 10.1038/s43856-021-00028-w.
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.
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。在此，我们讨论在基于机器学习的医学应用系统的不同开发步骤中减轻偏差的解决方案。
