Combining mathematical modelling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

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Abstract

Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficient anticoagulant and the appropriate dosage regimen remain a complex problem because of the intersubject variability in the coagulation kinetics and the effect of blood flow. The rapid assessment of the patient-specific response to anticoagulant regimens would assist clinical decision-making and ensure efficient management of coagulopathy. In this work, we introduce a novel approach that combines computational modelling and deep learning for the fast prediction of the patient-specific response to anticoagulant regimens. We extend a previously developed model to explore the spatio-temporal dynamics of thrombin generation and thrombus formation under anticoagulation therapy. Using a 1D version of the model, we generate a dataset of thrombus formation for thousands of virtual patients by varying key parameters in their physiological range. We use this dataset to train a artificial neural network (ANN) and we use it to predict patient’s response to anticoagulant therapy under flow. The algorithm is available and can be accessed though the link: https://github.com/MPS7/ML_coag. It yields an accuracy of 96 % which suggests that its usefulness can be assessed in a randomized clinical trial. The exploration of the model dynamics explains the decisions taken by the algorithm.Published by Elsevier Inc.