Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review

被引:60
作者
Comito, Carmela [1 ]
Pizzuti, Clara [1 ]
机构
[1] Inst High Performance Comp & Networking ICAR, Natl Res Council Italy CNR, Arcavacata Di Rende, Italy
关键词
COVID-19; Artificial intelligence; Machine learning; Deep learning; Forecasting; Diagnosing; RISK PREDICTION; MODEL; AI;
D O I
10.1016/j.artmed.2022.102286
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a compre-hensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Un-derstanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diag-nosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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页数:24
相关论文
共 152 条
  • [1] Artificial Intelligence in the Fight Against COVID-19: Scoping Review
    Abd-Alrazaq, Alaa
    Alajlani, Mohannad
    Alhuwail, Dari
    Schneider, Jens
    Al-Kuwari, Saif
    Shah, Zubair
    Hamdi, Mounir
    Househ, Mowafa
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (12)
  • [2] Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment
    Abdulkareem, Karrar Hameed
    Mohammed, Mazin Abed
    Salim, Ahmad
    Arif, Muhammad
    Geman, Oana
    Gupta, Deepak
    Khanna, Ashish
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (21) : 15919 - 15928
  • [3] A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients
    Ahamad, Md Martuza
    Aktar, Sakifa
    Rashed-Al-Mahfuz, Md
    Uddin, Shahadat
    Lio, Pietro
    Xu, Haoming
    Summers, Matthew A.
    Quinn, Julian M. W.
    Moni, Mohammad Ali
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 160
  • [4] Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea
    Al-qaness, Mohammed A. A.
    Ewees, Ahmed A.
    Fan, Hong
    Abualigah, Laith
    Abd Elaziz, Mohamed
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (10)
  • [5] Optimization Method for Forecasting Confirmed Cases of COVID-19 in China
    Al-qaness, Mohammed A. A.
    Ewees, Ahmed A.
    Fan, Hong
    Abd El Aziz, Mohamed
    [J]. JOURNAL OF CLINICAL MEDICINE, 2020, 9 (03)
  • [6] Alakus, CHAOS SOLITON FRACT, V140
  • [7] Alamo T., ARXIV200601731
  • [8] Aldhyani THH, COMPUTMATERCONTINUA, V67, P2141
  • [9] AlJame Maryam, 2020, Inform Med Unlocked, V21, P100449, DOI 10.1016/j.imu.2020.100449
  • [10] Amo-Boateng M., 2020, MEDRXIV 202006092012