Predictive models for COVID-19 detection using routine blood tests and machine learning

被引：12
|
作者：
Kistenev, Yury V. [1 ]
Vrazhnov, Denis A. [1 ]
Shnaider, Ekaterina E. [1 ]
Zuhayri, Hala [1 ]
机构：
[1] Tomsk State Univ, Lab Laser Mol Imaging & Machine Learning, 36 Lenin Ave, Tomsk 634050, Russia
来源：
HELIYON | 2022年 / 8卷 / 10期
关键词：
COVID-19; Blood tests; Machine learning;
D O I：
10.1016/j.heliyon.2022.e11185
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
引用
收藏
页数：11
相关论文
共 50 条
  • [1] COVID-19 diagnosis by routine blood tests using machine learning
    Matjaž Kukar
    Gregor Gunčar
    Tomaž Vovko
    Simon Podnar
    Peter Černelč
    Miran Brvar
    Mateja Zalaznik
    Mateja Notar
    Sašo Moškon
    Marko Notar
    [J]. Scientific Reports, 11
  • [2] COVID-19 diagnosis by routine blood tests using machine learning
    Kukar, Matjaz
    Guncar, Gregor
    Vovko, Tomaz
    Podnar, Simon
    Cernelc, Peter
    Brvar, Miran
    Zalaznik, Mateja
    Notar, Mateja
    Moskon, Saso
    Notar, Marko
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)
  • [3] Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests
    Cubukcu, Hikmet Can
    Topcu, Deniz Ilhan
    Bayraktar, Nilufer
    Gulsen, Murat
    Sari, Nuran
    Arslan, Ayse Hande
    [J]. AMERICAN JOURNAL OF CLINICAL PATHOLOGY, 2022, 157 (05) : 758 - 766
  • [4] Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
    Cabitza, Federico
    Campagner, Andrea
    Ferrari, Davide
    Di Resta, Chiara
    Ceriotti, Daniele
    Sabetta, Eleonora
    Colombini, Alessandra
    De Vecchi, Elena
    Banfi, Giuseppe
    Locatelli, Massimo
    Carobene, Anna
    [J]. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 2021, 59 (02) : 421 - 431
  • [5] Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning
    Lima, Tayna E.
    Ferraz, Matheus V. F.
    Brito, Carlos A. A.
    Ximenes, Pamella B.
    Mariz, Carolline A.
    Braga, Cynthia
    Wallau, Gabriel L.
    Viana, Isabelle F. T.
    Lins, Roberto D.
    [J]. ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS, 2024, 96 (02):
  • [6] Risk Stratification of COVID-19 Using Routine Laboratory Tests: A Machine Learning Approach
    Mlambo, Farai
    Chironda, Cyril
    George, Jaya
    [J]. INFECTIOUS DISEASE REPORTS, 2022, 14 (06) : 900 - 931
  • [7] Machine Learning Facemask Detection Models for COVID-19
    Puzi, Asmarani Ahmad
    Zainuddin, Ahmad Anwar
    Sahak, Rohilah
    Yunos, Muhammad Farhan Affendi Mohamad
    Rahman, Siti Husna Abdul
    Ramly, Munirah Mohd
    Maaz, Muhammad
    Kaitane, Wonderful Shammah
    [J]. 2022 IEEE INTERNATIONAL CONFERENCE ON SEMICONDUCTOR ELECTRONICS (ICSE 2022), 2022, : 148 - 151
  • [8] Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
    Davide Brinati
    Andrea Campagner
    Davide Ferrari
    Massimo Locatelli
    Giuseppe Banfi
    Federico Cabitza
    [J]. Journal of Medical Systems, 2020, 44
  • [9] Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
    Brinati, Davide
    Campagner, Andrea
    Ferrari, Davide
    Locatelli, Massimo
    Banfi, Giuseppe
    Cabitza, Federico
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2020, 44 (08)
  • [10] Effect of increased number of COVID-19 tests using supervised machine learning models
    Pooja, W.
    Snehal, N.
    Sonam, K.
    Wagh, S. R.
    Singh, N. M.
    [J]. 2021 AUSTRALIAN & NEW ZEALAND CONTROL CONFERENCE (ANZCC), 2021, : 131 - 136
12345