Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data › Research Explorer

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

Research output: Contribution to journal › Article › peer-review

DOI

https://doi.org/10.3389/fphys.2021.753282
Final published version

Svyatoslav Khamzin
Arsenii Dokuchaev
Anastasia Bazhutina
Tatiana Chumarnaya
Stepan Zubarev
Tamara Lyubimtseva
Viktoria Lebedeva
Dmitry Lebedev
Viatcheslav Gurev
Olga Solovyova

Original language	English
Article number	753282
Number of pages	21
Journal	Frontiers in Physiology
Volume	12
DOIs	https://doi.org/10.3389/fphys.2021.753282
Publication status	Published - 14 Dec 2021

Research areas

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

WoS ResearchAreas Categories

Physiology

ASJC Scopus subject areas

Physiology (medical)
Physiology

ID: 29207012