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A systematic review on AI/ML approaches against COVID-19 outbreak
Complex & Intelligent Systems ( IF 5.0 ) Pub Date : 2021-07-05 , DOI: 10.1007/s40747-021-00424-8
Onur Dogan 1, 2 , Sanju Tiwari 3 , M A Jabbar 4 , Shankru Guggari 5
Affiliation  

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.



中文翻译:

针对 COVID-19 爆发的 AI/ML 方法的系统评价

一种大流行病 COVID-19 感染了数百万人,在全世界造成了麻烦。由于人工智能 (AI) 和机器学习 (ML) 方法具有显着优势,因此针对 COVID-19 爆发的各种目的应用人工智能 (AI) 和机器学习 (ML) 方法的研究有所增加。尽管 AI/ML 应用程序为 COVID-19 疾病提供了令人满意的解决方案,但这些解决方案可能具有广泛的多样性。AI/ML 研究数量的增加和解决方案的多样性可能会混淆决定哪种 AI/ML 技术适用于哪些 COVID-19 目的。由于没有全面的综述研究,本研究对相关研究进行了系统的分析和总结。提出了一种研究方法来进行系统的文献综述,以制定研究问题、搜索标准和相关数据提取。最后,在遵循纳入和排除标准后,考虑了 264 项研究。这项研究可以被视为流行病和传播预测、诊断和检测以及药物/疫苗开发的关键要素。使用 COVID-19 中的 50 种 AI/ML 方法、用于患者结果预测的 8 种 AI/ML 方法、疾病预测中的 14 种 AI/ML 技术以及用于 COVID-19 风险评估的 5 种 AI/ML 方法探索了六个研究问题。它还涵盖药物开发中的 AI/ML 方法、COVID-19 疫苗、COVID-19 中的模型、数据集及其使用以及 AI/ML 的数据集应用。使用 COVID-19 中的 50 种 AI/ML 方法、用于患者结果预测的 8 种 AI/ML 方法、疾病预测中的 14 种 AI/ML 技术以及用于 COVID-19 风险评估的 5 种 AI/ML 方法探索了六个研究问题。它还涵盖药物开发中的 AI/ML 方法、COVID-19 疫苗、COVID-19 中的模型、数据集及其使用以及 AI/ML 的数据集应用。使用 COVID-19 中的 50 种 AI/ML 方法、用于患者结果预测的 8 种 AI/ML 方法、疾病预测中的 14 种 AI/ML 技术以及用于 COVID-19 风险评估的 5 种 AI/ML 方法探索了六个研究问题。它还涵盖药物开发中的 AI/ML 方法、COVID-19 疫苗、COVID-19 中的模型、数据集及其使用以及 AI/ML 的数据集应用。

更新日期:2021-07-05
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