为了推动满足所有人需求并使医疗保健民主化的健康创新，有必要评估各种分配转变的深度学习（DL）算法的概括性能，以确保这些算法具有强大的态度。据我们所知，这项回顾性研究是第一个开发和评估从跨种族，年龄和性别的长期跳动间隔的AF事件检测的深度学习模型（DL）模型的概括性能（DL）模型的概括。新的复发DL模型（表示为ARNET2）是在2,147名患者的大型回顾性数据集中开发的，总计51,386小时连续心电图（ECG）。对来自四个中心（美国，以色列，日本和中国）的手动注释测试集评估了模型的概括，总计402名患者。该模型在以色列海法的Rambam医院Holter Clinic的1,730个Consecutives Holter记录中进一步验证了该模型。该模型的表现优于最先进的模型，并且在种族，年龄和性别之间进行了广泛的良好。女性的表现高于男性和年轻人（不到60岁），并且在种族之间显示出一些差异。解释这些变化的主要发现是心房颤动患病率更高（AFL）的群体的性能受损。我们关于跨组的ARNET2相对性能的发现可能对选择相对于感兴趣群的首选AF检查方法具有临床意义。

Models in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in machine learning applications. It is often desirable for learning algorithms to take advantage of such structures, avoiding costly matrix computations that often require cubic time and quadratic storage. This is often accomplished by performing operations that maintain such structures, e.g. matrix inversion via the Sherman-Morrison-Woodbury formula. In this paper we consider the matrix square root and inverse square root operations. Given a low rank perturbation to a matrix, we argue that a low-rank approximate correction to the (inverse) square root exists. We do so by establishing a geometric decay bound on the true correction's eigenvalues. We then proceed to frame the correction has the solution of an algebraic Ricatti equation, and discuss how a low-rank solution to that equation can be computed. We analyze the approximation error incurred when approximately solving the algebraic Ricatti equation, providing spectral and Frobenius norm forward and backward error bounds. Finally, we describe several applications of our algorithms, and demonstrate their utility in numerical experiments.