To drive health innovation that meets the needs of all and democratize
healthcare, there is a need to assess the generalization performance of deep
learning (DL) algorithms across various distribution shifts to ensure that
these algorithms are robust. This retrospective study is, to the best of our
knowledge, the first to develop and assess the generalization performance of a
deep learning (DL) model for AF events detection from long term beat-to-beat
intervals across ethnicities, ages and sexes. The new recurrent DL model,
denoted ArNet2, was developed on a large retrospective dataset of 2,147
patients totaling 51,386 hours of continuous electrocardiogram (ECG). The
models generalization was evaluated on manually annotated test sets from four
centers (USA, Israel, Japan and China) totaling 402 patients. The model was
further validated on a retrospective dataset of 1,730 consecutives Holter
recordings from the Rambam Hospital Holter clinic, Haifa, Israel. The model
outperformed benchmark state-of-the-art models and generalized well across
ethnicities, ages and sexes. Performance was higher for female than male and
young adults (less than 60 years old) and showed some differences across
ethnicities. The main finding explaining these variations was an impairment in
performance in groups with a higher prevalence of atrial flutter (AFL). Our
findings on the relative performance of ArNet2 across groups may have clinical
implications on the choice of the preferred AF examination method to use
relative to the group of interest.

基于深度学习算法的 ArNet2 模型，跨不同族裔、年龄和性别，成功检测房颤事件，性能超过同类模型，且表现差异可能与房扑患病率有关。

适用于不同种族、年龄和性别的房颤诊断通用且鲁棒的深度学习算法

Generalizable and Robust Deep Learning Algorithm for Atrial Fibrillation Diagnosis Across Ethnicities, Ages and Sexes

Nationality identification unlocks important demographic information, with
many applications in biomedical and sociological research. Existing name-based
nationality classifiers use name substrings as features and are trained on
small, unrepresentative sets of labeled names, typically extracted from
Wikipedia. As a result, these methods achieve limited performance and cannot
support fine-grained classification.
We exploit the phenomena of homophily in communication patterns to learn name
embeddings, a new representation that encodes gender, ethnicity, and
nationality which is readily applicable to building classifiers and other
systems. Through our analysis of 57M contact lists from a major Internet
company, we are able to design a fine-grained nationality classifier covering
39 groups representing over 90% of the world population. In an evaluation
against other published systems over 13 common classes, our F1 score (0.795) is
substantial better than our closest competitor Ethnea (0.580). To the best of
our knowledge, this is the most accurate, fine-grained nationality classifier
available.
As a social media application, we apply our classifiers to the followers of
major Twitter celebrities over six different domains. We demonstrate stark
differences in the ethnicities of the followers of Trump and Obama, and in the
sports and entertainments favored by different groups. Finally, we identify an
anomalous political figure whose presumably inflated following appears largely
incapable of reading the language he posts in.

通过分析互联网公司的 5700 万个联系人列表，我们可以设计一个涵盖 90% 以上世界人口的 39 个代表细粒度国籍分类器，并将其应用于 Twitter 名人的追随者，演示了不同族裔追随者之间的鲜明差异。