Lessons (Machine) Learned From COVID-19

被引：2

作者：

Sullivan, Timothy ^{[1
,2
]}

机构：

[1] Icahn Sch Med Mt Sinai, Div Infect Dis, New York, NY USA

[2] Icahn Sch Med Mt Sinai, Div Infect Dis, One Gustave Levy Pl, Box 1090, New York, NY 10029 USA

来源：

JOURNAL OF INFECTIOUS DISEASES | 2024年 / 229卷 / 01期

关键词：

COVID-19; SARS-CoV-2; biomedical publishing; machine learning;

D O I：

10.1093/infdis/jiad224

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

100102 ;

摘要：

Since coronavirus disease 2019 (COVID-19) first emerged more than 3 years ago, more than 1200 articles have been written describing "lessons learned" from the pandemic. While these articles may contain valuable insights, reading them all would be impossible. A machine learning clustering analysis was therefore performed to obtain an overview of these publications and to highlight the benefits of using machine learning to analyze the vast and ever-growing COVID-19 literature.

引用

页码：7 / 9

页数：3