Division of Rheumatology, Department of Medicine, University of California, San Francisco.
Center for Population Health Sciences, Stanford University, Palo Alto, California.
JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. doi: 10.1001/jamainternmed.2018.3763.
A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning-based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.
机器学习在医疗保健中的一个承诺是避免诊断和治疗中的偏见；计算机算法可以客观地综合和解释医疗记录中的数据。将机器学习与临床决策支持工具（如计算机警报或诊断支持）集成，可为医生和其他提供医疗保健的人员提供有针对性和及时的信息，从而改善临床决策。然而，机器学习算法也可能存在偏见。这些偏见包括与数据缺失和算法未识别的患者、样本量和低估、分类错误和测量误差有关的偏见。人们担心机器学习算法使用的数据中的偏差和缺陷可能导致医疗保健中的社会经济差异。本特别通讯概述了可能引入基于机器学习的临床决策支持工具的潜在偏差，这些工具使用电子健康记录数据，并提出了一些潜在的解决方案，以解决过度依赖自动化、基于有偏差数据的算法以及不提供有临床意义的信息的算法等问题。现有的医疗保健差异不应因盲目或过度依赖机器而加剧。
