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Combining Computational Modelling and Machine Learning to Identify COVID-19 Patients with a High Thromboembolism Risk

Author

Listed:
  • Anass Bouchnita

    (Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA)

  • Anastasia Mozokhina

    (People’s Friendship University of Russia (RUDN), Moscow 117198, Russia)

  • Patrice Nony

    (Service de Pharmacologie Clinique, Hospices Civils de Lyon, 69002 Lyon, France
    UMR CNRS 5558, University Claude Bernard Lyon 1, 69100 Lyon, France)

  • Jean-Pierre Llored

    (Ecole Centrale Casablanca, Casablanca 20000, Morocco)

  • Vitaly Volpert

    (People’s Friendship University of Russia (RUDN), Moscow 117198, Russia
    Institut Camille Jordan, UMR 5208 CNRS, University Claude Bernard Lyon 1, 69622 Villeurbanne, France)

Abstract

Severe acute respiratory syndrome of coronavirus 2 (SARS-CoV-2) is a respiratory virus that disrupts the functioning of several organ systems. The cardiovascular system represents one of the systems targeted by the novel coronavirus disease (COVID-19). Indeed, a hypercoagulable state was observed in some critically ill COVID-19 patients. The timely prediction of thrombosis risk in COVID-19 patients would help prevent the incidence of thromboembolic events and reduce the disease burden. This work proposes a methodology that identifies COVID-19 patients with a high thromboembolism risk using computational modelling and machine learning. We begin by studying the dynamics of thrombus formation in COVID-19 patients by using a mathematical model fitted to the experimental findings of in vivo clot growth. We use numerical simulations to quantify the upregulation in the size of the formed thrombi in COVID-19 patients. Next, we show that COVID-19 upregulates the peak concentration of thrombin generation (TG) and its endogenous thrombin potential. Finally, we use a simplified 1D version of the clot growth model to generate a dataset containing the hemostatic responses of virtual COVID-19 patients and healthy subjects. We use this dataset to train machine learning algorithms that can be readily deployed to predict the risk of thrombosis in COVID-19 patients.

Suggested Citation

  • Anass Bouchnita & Anastasia Mozokhina & Patrice Nony & Jean-Pierre Llored & Vitaly Volpert, 2023. "Combining Computational Modelling and Machine Learning to Identify COVID-19 Patients with a High Thromboembolism Risk," Mathematics, MDPI, vol. 11(2), pages 1-13, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:289-:d:1026643
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    References listed on IDEAS

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    1. Anastasia Mozokhina & Anass Bouchnita & Vitaly Volpert, 2021. "Blood Clotting Decreases Pulmonary Circulation during the Coronavirus Disease," Mathematics, MDPI, vol. 9(19), pages 1-19, September.
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