Age, sex and race bias in automated arrhythmia detectors

• Published in: Journal of electrocardiology Vol. 74; pp. 5 - 9
• Main Authors: Perez Alday, Erick A., Rad, Ali B., Reyna, Matthew A., Sadr, Nadi, Gu, Annie, Li, Qiao, Dumitru, Mircea, Xue, Joel, Albert, Dave, Sameni, Reza, Clifford, Gari D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2022
• Subjects: Age; Age Factors; Arrhythmias, Cardiac; Bias; Cardiovascular; Electrocardiography; Female; Healthcare; Humans; Machine learning; Male; Race; Sex; Sex Factors; Healthcare; Bias; Age; Race; Sex; Machine learning

Despite the recent explosion of machine learning applied to medical data, very few studies have examined algorithmic bias in any meaningful manner, comparing across algorithms, databases, and assessment metrics. In this study, we compared the biases in sex, age, and race of 56 algorithms on over 130...