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

被引:71
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.
引用
收藏
页数:24
相关论文
共 152 条
[1]   Artificial Intelligence in the Fight Against COVID-19: Scoping Review [J].
Abd-Alrazaq, Alaa ;
Alajlani, Mohannad ;
Alhuwail, Dari ;
Schneider, Jens ;
Al-Kuwari, Saif ;
Shah, Zubair ;
Hamdi, Mounir ;
Househ, Mowafa .
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 [J].
Abdulkareem, Karrar Hameed ;
Mohammed, Mazin Abed ;
Salim, Ahmad ;
Arif, Muhammad ;
Geman, Oana ;
Gupta, Deepak ;
Khanna, Ashish .
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 [J].
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 .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 160
[4]   Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea [J].
Al-qaness, Mohammed A. A. ;
Ewees, Ahmed A. ;
Fan, Hong ;
Abualigah, Laith ;
Abd Elaziz, Mohamed .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (10)
[5]   Optimization Method for Forecasting Confirmed Cases of COVID-19 in China [J].
Al-qaness, Mohammed A. A. ;
Ewees, Ahmed A. ;
Fan, Hong ;
Abd El Aziz, Mohamed .
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