@article{b2e330f16d4c437abc4715c65166fa23,
title = "Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data",
keywords = "cardiac modeling, cardiac resynchronization therapy, electrophysiology, heart failure, hybrid approach, machine learning, prediction, EXCITATION, CRT, DYSSYNCHRONY, HEART, INTELLIGENCE, RESYNCHRONIZATION THERAPY, ELECTROPHYSIOLOGY, VENTRICULAR LEAD IMPLANTATION, MAGNETIC-RESONANCE, DEFIBRILLATOR",
author = "Svyatoslav Khamzin and Arsenii Dokuchaev and Anastasia Bazhutina and Tatiana Chumarnaya and Stepan Zubarev and Tamara Lyubimtseva and Viktoria Lebedeva and Dmitry Lebedev and Viatcheslav Gurev and Olga Solovyova",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 Khamzin, Dokuchaev, Bazhutina, Chumarnaya, Zubarev, Lyubimtseva, Lebedeva, Lebedev, Gurev and Solovyova.",
year = "2021",
month = dec,
day = "14",
doi = "10.3389/fphys.2021.753282",
language = "English",
volume = "12",
journal = "Frontiers in Physiology",
issn = "1664-042X",
publisher = "Frontiers Media S.A.",
}