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Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

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Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

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Rodríguez-Belenguer, P.; Kopańska, K.; Llopis Lorente, J.; Trénor Gomis, BA.; Saiz Rodríguez, FJ.; Pastor, M. (2022). Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia. http://hdl.handle.net/10251/183067

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Title: Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia
Author: Rodríguez-Belenguer, Pablo Kopańska, Karolina Llopis Lorente, Jordi Trénor Gomis, Beatriz Ana Saiz Rodríguez, Francisco Javier Pastor, Manuel
UPV Unit: Universitat Politècnica de València. Centro de Investigación e Innovación en Bioingeniería - Centre de Recerca i Innovació en Bioenginyeria
Issued date:
Abstract:
In cardiotoxicity studies it is common to pre-compute the values of different biomarkers (my equation or TX) for a range of ion channel blockades. Since every simulation requires costly computations, to complete the matrix ...[+]
Copyrigths: Reconocimiento - No comercial (by-nc)
Publisher:
Universitat Politècnica de València
Project ID:
info:eu-repo/grantAgreement/EC/H2020/101016496/EU/Simulation of Cardiac Devices & Drugs for in-silico Testing and Certification/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2020/043
FPU18/01659
info:eu-repo/grantAgreement/AEI//FPU%2F18/016
info:eu-repo/grantAgreement/AEI//FPU18%2F01659
Type: Artículo

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