Predicting COVID-19 with AI techniques: current research and future directions

被引：0

作者：

Comito, Carmela ^{[1
]}

Pizzuti, Clara ^{[1
]}

机构：

[1] Natl Res Council Italy CNR, ICAR Inst, Via Pietro Bucci 8-9 C, Arcavacata Di Rende, CS, Italy

来源：

PROCEEDINGS OF THE 2021 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, ASONAM 2021 | 2021年

关键词：

COVID-19; Artificial Intelligence; Machine Learning; Deep Learning; Forecasting;

D O I：

10.1145/3487351.3490958

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Artificial Intelligence (AI), since the onset of the COVID-19 pandemic at the beginning of the last year, is playing an important role in supporting physicians and health authorities in different difficult tasks such as virus spreading, patient diagnosing and monitoring, contact tracing. In this paper, we provide an overview of the methods based on AI technologies proposed for COVID-19 forecasting. Summary statistics of the techniques adopted by researchers, categorized on the base of the underlying AI sub-area, are reported, along with publication venue of papers. The effectiveness of these approaches is investigated and their capabilities or weaknesses in providing reliable predictions are discussed. Future challenges are finally analyzed and research directions for improving current tools are suggested.

引用

页码：518 / 524

页数：7

共 41 条

[1] 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

[2] 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

[3] Comparison of deep learning approaches to predict COVID-19 infection [J].

Alakus, Talha Burak ;

Turkoglu, Ibrahim .

CHAOS SOLITONS & FRACTALS, 2020, 140

[4]

AlJame Maryam, 2020, Inform Med Unlocked, V21, P100449, DOI 10.1016/j.imu.2020.100449

[5]

[Anonymous], 2020, COVID-19 data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

[6] COVID-19 Outbreak Prediction with Machine Learning [J].

Ardabili, Sina F. ;

Mosavi, Amir ;

Ghamisi, Pedram ;

Ferdinand, Filip ;

Varkonyi-Koczy, Annamaria R. ;

Reuter, Uwe ;

Rabczuk, Timon ;

Atkinson, Peter M. .

ALGORITHMS, 2020, 13 (10)

[7] Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms [J].

Arpaci, Ibrahim ;

Huang, Shigao ;

Al-Emran, Mostafa ;

Al-Kabi, Mohammed N. ;

Peng, Minfei .

MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (08) :11943-11957

[8] Utilization of machine-learning models to accurately predict the risk for critical COVID-19 [J].

Assaf, Dan ;

Gutman, Ya'ara ;

Neuman, Yair ;

Segal, Gad ;

Amit, Sharon ;

Gefen-Halevi, Shiraz ;

Shilo, Noya ;

Epstein, Avi ;

Mor-Cohen, Ronit ;

Biber, Asaf ;

Rahav, Galia ;

Levy, Itzchak ;

Tirosh, Amit .

INTERNAL AND EMERGENCY MEDICINE, 2020, 15 (08) :1435-1443

[9] Bagging predictors [J].

Breiman, L .

MACHINE LEARNING, 1996, 24 (02) :123-140

[10] Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study [J].

Brinati, Davide ;

Campagner, Andrea ;

Ferrari, Davide ;

Locatelli, Massimo ;

Banfi, Giuseppe ;

Cabitza, Federico .

JOURNAL OF MEDICAL SYSTEMS, 2020, 44 (08)

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