Predicting Heart Failure. Группа авторов
Graphite, 29
Chapter 4
| AC | Alternative Current, 9 |
| BSN | Body Sensor Network, 2 CE Counter Electrode, 5 |
| CMT | Coupled-Mode Theory, 12 |
| EDAS | European Aeronautic Defense and Space Company, 10 |
| EM | Electromagnetic Interference, 11 |
| EMF | Electromagnetic Field, 9 |
| HPF | High-Pass Filter, 17 |
| IoT | Internet of Things, 2 |
| LPF | Low Pass Filter, 17 |
| MEMS | Micro-Electromechanical Systems, 4 |
| MPT | Microwave Power Transmission, 8 |
| RE | Reference Electrode, 5 |
| RF | Radio Frequency, 3 |
| SHM | Structural Health Supervising, 2 |
| SoC | System on a Chip, 5 |
| VSWR | Voltage Standing Wave Ratio, 24 |
| WBAN | Wireless Body Area Network, 2 |
| WE | Working Electrode, 5 |
Chapter 5
| BCG | Ballistocardiography, 13 |
| BLUE | Bedside Lung Ultrasound, 10 |
| BNP | B-type Natriuretic Peptide, 14 |
| C.A.USE | Cardiac Arrest Ultrasound Exam, 10 |
| CHF | Chronic Heart Failure, 2 |
| DNN | Deep Neural Network, 12 |
| ECG | Electrocardiography, 3 |
| EVLW | Extravascular Lung Water, 8 |
| FALLS | Fluid Administration Limited by Lung Sonography, 10 |
| GPS | Global Positioning System, 6 |
| HF | Heart failure, 2 |
| ICD | Implantable Cardioverter Defibrillator, 12 |
| LuCUS | Lung and Cardiac Ultrasound, 10 |
| LUS | Lung Ultrasound, 8 |
| LV | Left Ventricular, 7 |
| MEMS | Microelectromechanical System, 6 |
| MFCCs | Mel-frequency Cepstral Coefficients, 12 |
| NT-proBNP | Amino-terminal Pro-B-type Natriuretic Peptide, 14 |
| PA | Pulmonary Arterial, 7 |
| PCG | Phonocardiogram, 12 |
| PPG | Photoplethysmogram, 14 |
| RCTs | Randomized Control Trials, 15 |
| ReDS | Remote Dielectric Sensing Technology, 5 |
| RPM | Remote Patient Monitoring, 15 |
| RV | Right Ventricular, 7 |
| SCG | Seismocardiography, 12 |
Chapter 6
| ACM | All-cause Mortality, 8 |
| AI | Artificial Intelligence, 3, 8 |
| ANN | Artificial Neural Networks, 3,12 |
| AUC | Area Under the Curve, 9 |
| CAD | Coronary Artery Disease, 8 |
| CCTA | Cardiac Computed Tomographic Angiography, 8 |
| CMR | Cardiac Magnetic Resonance, 12 |
| CNN | Convolutional Neural Networks, 12 |
| DL | Deep learning, 12 |
| ECG | Electrocardiogram, 9 |
| FDA | Food and Drug Administration, 17 |
| FFR | Fractional Flow Reserve, 9 |
| FRS | Framingham Risk Score, 8 |
| GAN | Generative Adversarial Networks, 11 |
| GAN | Generative Adversarial Networks, 3 |
| HFpEF | Heart Failure with Preserved Ejection Fraction, 10 |
| HMM | Hidden Markov Model, 15 |
| LASSO | Least Absolute Shrinkage and Selection Operator, 3 |
| LV | Left Ventricle, 9 |
| LVEF | Left Ventricular Ejection Fraction, 12 |
| MDI | Modified Duke Index, 8 |
| MFR | Myocardial Flow Reserve, 9 |
| ML | Machine Learning, 3 |
| MPI | Myocardial Perfusion Imaging, 15 |
| MPS | Myocardial Perfusion Scan, 9 |
| NER | Names Entity Recognition, 15 |
| NLP | Natural Language Processing, 15 |
| POS | Part of Speech, 15 |
| PSA | Parsing or Syntactic Analysis, 15 |
| RNN | Recurrent Neural Networks, 12 |
| SIS | Segment Involvement Score, 8 |
| SSS | Segment Stenosis Score, 8 |
| STE | Speckle-tracking Echocardiography, 11 |
| SVMs | Support Vector Machines, 8 |
| TPD | Total Perfusion Deficit, 15 |
| TTE | Transthoracic Echocardiogram, 12 |
Chapter 7
| DT | Decision Tree, 2 |
| K-NN | K-Nearest Neighbor, 5 |
| LDA | Linear Discriminant Analysis, 2 |
| MAFIA | Maximal Frequent Itemset Algorithm, 4 |
| NB | Naïve Bayes, 2 |
| NN | Neural Networks, 4 |
| RF | Random Forest, 2 |
| SVM | Support Vector Machine, 2 |
Chapter 8
| AUPRC | Area Under Precision Recall curve, 10 |
| AUROC | Area Under ROC Curve, 10 |
| BNP | Brain Natriuretic Peptide, 4 |
| CNN | Convolutional Neural Networks, 7 |
| DAE | Denoising Autoencoder, 7 |
| DBNs | Deep Belief Networks, 7 |
| DT | Decision Tree, 7 |
| ECG | Electrocardiogram, 3 |
| EHR | Electronic Health Records, 5 |
| EMB | Endomyocardial Biopsy, 7 |
| ESC | European Society of Cardiology, 4 |
| GBM | Gradient-boosted Model, 11 |
| GRU RNNs | Gated Recurrent Unit Recurrent Neural Networks, 9 |
| GWTG-HF | Get With The Guidelines-Heart Failure, 11 |
| HF | Heart Failure, 2 |
| KNNs | K-nearest Neighbors, 7 |
| LAD | Left Atrial Dimension, 10 |
| LR | Logistic Regression, 11 |
| LSTM | Long Short-Term Memory, 7 |
| MAGGIC | Meta-Analysis Global Group in Chronic, 11 |
| ML | Machine Learning, 2 |
| ReLU | Rectified Linear Unit, 8 |
| RETAIN | REverse Time AttentIoN Model, 9 |
| RF | Random Forest, 8 |
| RNN | recurrent Neural Networks, 7 |
| ROI | Regions of Interests, 8 |
| RPM | Remote Patient Monitoring, 5 |
| SVM | Support Vector Machine, 7 |
| TAN | Tree-augmented Naive Bayesian, 11 |
Chapter 9
| AF | Atrial Fibrillation, 9 |
| AFL | Atrial Flutter, 10 |
| AUC | Area Under the Roc Curve, 8 |
| BN | Bayes Network, 31 |
| CART | Classification and Regression Trees, 15 |
| CFS | Correlation-based Feature Selection, 31 |
| CMR | Cardiac Magnetic Resonance, 6 |
| CNN | Convolutional Neural Network, 9 |
| CTCA | Computed Tomography Coronary Angiography, 6 |
| DL | Deep Learning, 8 |
| DNN | Deep Neural Network, 9 |
| DUNs | Deep Unified Networks, 20 |
| ECG | Electrocardiogram, 6 |
| EF | Ejection Fraction, 5 |
| EFFECT | Enhanced Feedback for Effective Cardiac Treatment, 10 |
| EHR | Electronic Health Record, 8 |
| ELM | Extreme Learning Machine, 10 |
| ESC | European Society of Cardiology, 6 |
| GDS | Generalized Discriminant Analysis, 16 |
| GLM | Generalized Linear Model, 23 |
| HF | Heart Failure, 1 |
| HFmrEF | HF with Mid-range or Mildly Reduced EF, 5 |
| HFpEF | Heart Failure with Preserved Ejection Fraction, 5 |
| HFrEF | Heart Failure with Reduced Ejection Fraction, 5 |
| HRV | Heart Rate Variability, 9 |
| K-NN | K-nearest Neighbor, 8 |
| LGE | Late Gadolinium Enhancement, 6 |
| LMT | Logistic Model Trees, 31 |
| LR | Logistic Regression, 8 |
| LS-SVM | Least Square SVM, 9 |
| LSTM | Long Short-term |