@文章{信息:doi/10.2196/22400，作者=“Allen, Angier和Mataraso, Samson和Siefkas, Anna和Burdick, Hoyt和Braden, Gregory和Dellinger, R Phillip和McCoy, Andrea和Pellegrini, Emily和Hoffman, Jana和Green-Saxena, Abigail和Barnes, Gina和Calvert, Jacob和Das, Ritankar”，标题=“预测死亡率的种族无偏见的机器学习方法:，期刊="JMIR公共卫生监测"，年="2020"，月=" 10月"，日="22"，卷="6"，数="4"，页数="e22400"，关键词="机器学习";健康差异;种族差异;死亡率;背景:在美国，医疗保健中的种族差异是有据可查的。随着机器学习方法在医疗保健环境中变得越来越普遍，重要的是要确保这些方法不会因为有偏见的预测或种族群体之间的差异准确性而导致种族差异。目的:本研究的目标是评估一种机器学习算法，该算法旨在最大限度地减少白人和非白人患者组之间住院死亡率预测的偏差。方法:对算法训练数据进行预处理，使偏差最小化。我们对2001年至2012年间在一家大型学术健康中心重症监护病房(ICU)住院的患者的电子健康记录数据进行了回顾性分析，数据来自重症监护医疗信息集市-III数据库。 Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). Results: The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). Conclusions: This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods. ", issn="2369-2960", doi="10.2196/22400", url="http://publichealth.www.mybigtv.com/2020/4/e22400/", url="https://doi.org/10.2196/22400", url="http://www.ncbi.nlm.nih.gov/pubmed/33090117" }