Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abhijit Dasgupta, Abhisek Bakshi, Srijani Mukherjee, Kuntal Das, Soumyajeet Talukdar, Pratyayee Chatterjee, Sagnik Mondal, Puspita Das, Subhrojit Ghosh, Archisman Som, Pritha Roy, Rima Kundu, Akash Sarkar, Arnab Biswas, Karnelia Paul, Sujit Basak, Krishnendu Manna, Chinmay Saha, Satinath Mukhopadhyay, Nitai P Bhattacharyya, Rajat K De
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引用次数: 5

Abstract

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.

Abstract Image

大流行冠状病毒病(COVID-19)的流行病学挑战:人工智能的作用。
由于由严重急性呼吸综合征冠状病毒分支2引起的冠状病毒(COVID-19)大流行,世界正在经历一场重大卫生灾难。科学界面临的首要挑战是探索病毒的生长和传播能力。人工智能(AI),如深度学习,在以下方面的应用(i)从x射线或计算机断层扫描(CT)或高分辨率CT (HRCT)图像中快速检测疾病,(ii)准确预测全球流行病模式及其饱和度,(iii)根据社交网络数据预测疾病和对人口的心理影响,以及(iv)预测药物-蛋白质相互作用以重新利用药物,引起了广泛关注。在本研究中,我们描述了各种基于人工智能的技术在CT图像快速有效检测中的作用,补充了定量实时聚合酶链反应和免疫诊断分析。还讨论了基于人工智能的预测当前大流行模式、防止疾病传播和口罩检测的技术。我们根据不同的因素来检查病毒的传播方式。我们研究了深度学习技术来评估最可能治疗COVID-19的药物的亲和力。本文分类如下:应用领域>医疗保健算法开发>生物数据挖掘技术>机器学习。
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
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
期刊介绍: The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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