bebi 21(2): e6

Research Article

Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients

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  • @ARTICLE{10.4108/eai.12-3-2021.169028,
    author={Anđela Blagojević and Tijana Šušteršič and Ivan Lorencin and Sandi Baressi Šegota and Dragan Milovanović and Danijela Baskić and Dejan Baskić and Zlatan Car and Nenad Filipović},
    title={Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients},
    journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics},
    volume={1},
    number={2},
    publisher={EAI},
    journal_a={BEBI},
    year={2021},
    month={3},
    keywords={COVID-19, machine learning, personalized model, U-net, classification, predictive models, finite element simulation},
    doi={10.4108/eai.12-3-2021.169028}
}
  • Anđela Blagojević
    Tijana Šušteršič
    Ivan Lorencin
    Sandi Baressi Šegota
    Dragan Milovanović
    Danijela Baskić
    Dejan Baskić
    Zlatan Car
    Nenad Filipović
    Year: 2021
    Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients
    BEBI
    EAI
    DOI: 10.4108/eai.12-3-2021.169028
Anđela Blagojević1,2, Tijana Šušteršič1,2, Ivan Lorencin3, Sandi Baressi Šegota3, Dragan Milovanović4,5, Danijela Baskić4, Dejan Baskić5,6, Zlatan Car3, Nenad Filipović1,2,*
  • 1: University of Kragujevac, Faculty of Engineering, Sestre Janjić 6, 34000 Kragujevac, Serbia
  • 2: Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
  • 3: University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia
  • 4: Clinical Centre Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
  • 5: University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000 Kragujevac, Serbia
  • 6: Institute of Public Health Kragujevac, Nikole Pašića 1, 34000 Kragujevac, Serbia
*Contact email: fica@kg.ac.rs

Abstract

INTRODUCTION: Machine learning algorithms and in silico models for the COVID-19 have been used to classify infectious people and predict their condition in time.

OBJECTIVES: This study aims at creating a personalized model that combines machine learning and finite element simulation approach in order to predict development of COVID-19 infection in patients.

METHODS: The methodology combines several aspects (1) classification of patients into several classes of clinical condition (2) segmentation of human lungs in X ray images (3) finite element simulation to investigate the spreading of SARS-COV-2 virion in the lungs.

RESULTS: The findings show accuracy larger than 90% in all aspects of methodology. FE simulation has revealed that the distribution of airflow in the lung changes in time with the infection.

CONCLUSION: The key benefit of our proposed method is that it combines several methods that will be further improved in order to create a truly unique combined methodology for predictive models in patients infected with COVID-19.

Keywords
COVID-19, machine learning, personalized model, U-net, classification, predictive models, finite element simulation
Received
2021-03-06
Accepted
2021-03-11
Published
2021-03-12
Publisher
EAI
http://dx.doi.org/10.4108/eai.12-3-2021.169028

Copyright © 2021 Anđela Blagojević et al., licensed to EAI . This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.