Mottaqi Mohammad Sadeq, Mohammadipanah Fatemeh, Sajedi Hedieh
Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran.
Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, 14155-6455, Tehran, Iran.
Inform Med Unlocked. 2021;23:100526. doi: 10.1016/j.imu.2021.100526. Epub 2021 Jan 24.
The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI).
This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2).
A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made.
For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models.
While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.
最近出现的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染使全球陷入异常严峻的形势，迫切需要通过人工智能（AI）制定有效的应对措施。
本文旨在概述机器学习技术在冠状病毒病（SARS-CoV-2）预防、诊断、监测和治疗方面的最新应用。
对截至2020年11月的近期出版物进行了逐步研究，这些出版物涉及应对2019冠状病毒病（COVID-19）感染挑战的人工智能方法。
在患者诊断和筛查方面，卷积神经网络（CNN）和支持向量机（SVM）广泛应用于分类。此外，深度神经网络（DNN）和同源建模是最常用的SARS-CoV-2药物重新利用模型。
虽然通过医学图像处理诊断SARS-CoV-2感染及其通过机器学习的传播模式等领域已得到充分研究，但患者治疗结果和药物开发等一些领域需要使用人工智能方法进一步研究。
