Breast cancer screening during the COVID-19 Pandemic in the United States: Results from real-world health records data

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2023-11-01 DOI:10.1370/afm.22.s1.4885

William Curry, Wen-Jan Tuan, Qiushi Chen, Andrew Chung

引用次数: 0

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美国 COVID-19 大流行期间的乳腺癌筛查：来自真实世界健康记录数据的结果

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.