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

Anass Bouchnita (), Anastasia Mozokhina, Patrice Nony, Jean-Pierre Llored and Vitaly Volpert
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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
Jean-Pierre Llored: Ecole Centrale Casablanca, Casablanca 20000, Morocco
Vitaly Volpert: People’s Friendship University of Russia (RUDN), Moscow 117198, Russia

Mathematics, 2023, vol. 11, issue 2, 1-13

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.

Keywords: blood coagulation; thrombosis; Navier–Stokes equations; computational fluid dynamics; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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