The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.
The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.
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人工智能和数据整合在Covid-19研究中的应用：范围审查。
DOI：
10.1093/jamia/ocab098
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发表时间：
2021-08-13
期刊：
影响因子：
--
通讯作者：
Bian J
中科院分区：
文献类型：
--
作者：
Guo Y;Zhang Y;Lyu T;Prosperi M;Wang F;Xu H;Bian J
总结人工智能（AI）如何应用于COVID-19研究，并确定这些AI应用是否整合了来自不同来源的异构数据进行建模。我们于2021年3月9日检索了2个主要的COVID-19文献数据库，即美国国立卫生研究院的LitCovid和世界卫生组织的COVID-19数据库。按照系统性综述和荟萃分析的首选报告项目（PRISMA）指南，2名评价者独立审查了2轮筛选中的所有文章。在最终定性分析中纳入的794项研究中，我们确定了应用AI的7个关键COVID-19研究领域，包括疾病预测，基于医学成像的诊断和预后，早期检测和预后（非成像），药物再利用和早期药物发现，社交媒体数据分析，基因组，转录组和蛋白质组数据分析，以及其他COVID-19研究主题。我们还发现，这些AI应用程序中缺乏异构数据集成。与COVID-19结果相关的风险因素存在于异构数据源中，包括电子健康记录、监测系统、社会人口数据集等等。然而，COVID-19研究中的大多数人工智能应用都采用了单一来源的方法，这可能会忽略重要的风险因素，从而导致有偏见的算法。集成异构数据进行建模将有助于实现AI算法的全部潜力，提高精度并减少偏差。COVID-19研究中的人工智能应用缺乏数据集成，需要一个多层次的人工智能框架来支持对来自不同来源的异构数据的分析。
To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
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DOI：
10.1016/j.scitotenv.2020.142723
发表时间：
2021-04-15
期刊：
The Science of the total environment
影响因子：
--
作者：
Chakraborti S;Maiti A;Pramanik S;Sannigrahi S;Pilla F;Banerjee A;Das DN
通讯作者：
Das DN
影响因子：
7.4
作者：
Cao Z;Tang F;Chen C;Zhang C;Guo Y;Lin R;Huang Z;Teng Y;Xie T;Xu Y;Song Y;Wu F;Dong P;Luo G;Jiang Y;Zou H;Chen YQ;Sun L;Shu Y;Du X
通讯作者：
Du X
DOI：
10.3390/ijerph17093176
发表时间：
2020-05-01
影响因子：
--
作者：
Bragazzi, Nicola Luigi;Dai, Haijiang;Wu, Jianhong
通讯作者：
Wu, Jianhong
DOI：
10.3390/ijerph18010268
发表时间：
2020-12-31
影响因子：
--
作者：
Asgary A;Valtchev SZ;Chen M;Najafabadi MM;Wu J
通讯作者：
Wu J
影响因子：
7.7
作者：
Burdick, Hoyt;Lam, Carson;Das, Ritankar
通讯作者：
Das, Ritankar