Ventricular geometry–regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography

Keyphrases

• Echocardiography 100%
• QRS Duration 100%
• Machine Learning 100%
• Cardiac Resynchronization Therapy Response 100%
• Relative Wall Thickness 55%
• CRT-D 33%
• Nonischemic Cardiomyopathy 33%
• Left Bundle Branch Block 33%
• Feature Combination 33%
• Therapy Response Prediction 33%
• Preimplantation 22%
• Support Vector Machine 11%
• Posterior Wall Thickness 11%
• Cardiac Resynchronization Therapy 11%
• Female Gender 11%
• Classification Accuracy 11%
• Clinical Laboratory 11%
• Classification Algorithms 11%
• Ventricular Remodeling 11%
• Analytical Approach 11%
• Machine Learning Algorithms 11%
• Triplet 11%
• Categorical Variables 11%
• Current Guidelines 11%
• Septal Wall Thickness 11%
• Ischemic Cardiomyopathy 11%
• Machine Learning Models 11%
• Support Vector Machine Model 11%
• K-nearest Neighbor (K-NN) 11%
• Random Subspace 11%

Medicine and Dentistry

• Treatment Response 100%
• Cardiac Resynchronization Therapy 100%
• Echocardiography 100%
• QRS Interval 100%
• Electrocardiography 100%
• Nonischemic Cardiomyopathy 23%
• Left Bundle Branch Block 23%
• Heart Ventricle Remodeling 7%
• Defibrillator 7%
• Ischemic Cardiomyopathy 7%
• Pacemaker 7%

Biochemistry, Genetics and Molecular Biology

• Cardiac Resynchronization Therapy 100%
• QRS Interval 100%
• Electrocardiography 100%
• Left Bundle Branch Block 23%
• Support Vector Machine 15%
• Preimplantation 15%
• Classification Algorithm 7%
• K Nearest Neighbor 7%