15 citations · 17 across the 4 of their papers we have counts for
4 papers
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Kristoffer Larsen, Chen Zhao, Joyce Keyak +7
Aims. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This…
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Zhuo He, Hongjin Si, Xinwei Zhang +3
Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting wh…
A new method using deep learning to predict the response to cardiac resynchronization therapy
Kristoffer Larsena, Zhuo He, Chen Zhao +8
Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchroni…
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Zhuo He, Xinwei Zhang, Chen Zhao +5
Background. Studies have shown that the conventional left ventricular mechanical dyssynchrony (LVMD) parameters have their own statistical limitations. The purpose of this study is…