Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.
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DOI：
10.1001/jamainternmed.2018.3763
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发表时间：
2018-11-01
影响因子：
39
通讯作者：
Schmajuk G
中科院分区：
文献类型：
--
作者：
Gianfrancesco MA;Tamang S;Yazdany J;Schmajuk G
医疗保健领域机器学习的一个前景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病案中的数据。将机器学习与临床决策支持工具（如计算机化警报或诊断支持）相结合，可以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受到偏见的影响。这些偏差包括与数据缺失和未被算法识别的患者、样本量和低估、错误分类和测量误差有关的偏差。人们担心，机器学习算法使用的数据存在偏差和缺陷，可能会导致医疗保健领域的社会经济差异。本特别通讯概述了可能引入使用电子健康记录数据的基于机器学习的临床决策支持工具的潜在偏差，并提出了过度依赖自动化、基于有偏差数据的算法以及不提供临床有意义信息的算法等问题的潜在解决方案。现有的医疗保健差距不应因轻率或过度依赖机器而扩大。
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.
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影响因子：
15.2
作者：
Rajkomar, Alvin;Oren, Eyal;Dean, Jeffrey
通讯作者：
Dean, Jeffrey
影响因子：
9.7
作者：
Ng, Judy H.;Ye, Faye;Scholle, Sarah Hudson
通讯作者：
Scholle, Sarah Hudson
DOI：
10.1056/nejmp1714229
发表时间：
2018-03-15
期刊：
The New England journal of medicine
影响因子：
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作者：
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通讯作者：
Magnus D
影响因子：
3.6
作者：
Arpey, Nicholas C.;Gaglioti, Anne H.;Rosenbaum, Marcy E.
通讯作者：
Rosenbaum, Marcy E.
影响因子：
56.9
作者：
Caliskan, Aylin;Bryson, Joanna J.;Narayanan, Arvind
通讯作者：
Narayanan, Arvind