René van Es
researcher
Facts
6 properties- Instance of
- human
- Date of birth
- 2000
- Occupation
- researcher
- Employer
- University Medical Center Utrecht (2019–present)[3]
🏷️ Also known as
en René van Es
🔖 Identifiers · 4
- ORCID iD
- 0000-0001-9950-4388
- Dimensions author ID
- 01104733700.77
- LinkedIn personal profile ID
- revaes
- Scopus author ID
- 56142070400
🔗 Connections
Author of 10
Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks,
Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks,
Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning,
ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy,
Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy,
Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning,
Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders,
Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks
+2 more
← Authored by 10
Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks,
Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks,
Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning,
ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy,
Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy,
Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning,
Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders,
Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks
+2 more
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