deep network designer app Search Results


deep network designer toolbox  (MathWorks Inc)
96
MathWorks Inc deep network designer toolbox
Deep Network Designer Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab 2020a release  (MathWorks Inc)
96
MathWorks Inc matlab 2020a release
Matlab 2020a Release, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab 2020a release - by Bioz Stars, 2026-06
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armagosa desert research site  (geological survey network)
90
geological survey network armagosa desert research site
Armagosa Desert Research Site, supplied by geological survey network, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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original standard pee dee belemnite  (geological survey network)
90
geological survey network original standard pee dee belemnite
Original Standard Pee Dee Belemnite, supplied by geological survey network, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep autoencoder network  (Nonlinear Dynamics)
86
Nonlinear Dynamics deep autoencoder network
Deep Autoencoder Network, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep autoencoder network - by Bioz Stars, 2026-06
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deep unified networks (duns)  (Partners HealthCare System Inc)
90
Partners HealthCare System Inc deep unified networks (duns)
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
Deep Unified Networks (Duns), supplied by Partners HealthCare System Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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2023 deep network designer toolbox  (MathWorks Inc)
96
MathWorks Inc 2023 deep network designer toolbox
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
2023 Deep Network Designer Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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2023 deep network designer toolbox - by Bioz Stars, 2026-06
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assay design3 1  (Sequenom)
86
Sequenom assay design3 1
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
Assay Design3 1, supplied by Sequenom, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 86 stars, based on 1 article reviews
assay design3 1 - by Bioz Stars, 2026-06
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designi g control systems  (MathWorks Inc)
96
MathWorks Inc designi g control systems
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
Designi G Control Systems, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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designi g control systems - by Bioz Stars, 2026-06
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design x software  (Geomagic Inc)
86
Geomagic Inc design x software
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
Design X Software, supplied by Geomagic Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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mirna primer design tool  (PrimerDesign Inc)
90
PrimerDesign Inc mirna primer design tool
Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco
Mirna Primer Design Tool, supplied by PrimerDesign Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco

Journal: Cureus

Article Title: Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review

doi: 10.7759/cureus.73876

Figure Lengend Snippet: Summary of the included studies Abbreviations: NN:Neural Networks; SVM:Support Vector Machine; RF:Random Forest; DT:Decision Tree; CART:Classification and Regression Tree; kNN:k-Nearest Neighbor; LR:Logistic Regression; CNN:Convolutional Neural Network; LMT:Logistic Model Tree; ROT:Rotation Forest; XGBoost:eXtreme Gradient Boosting; Adaboost:Adaptive Boosting; LEBoosting:Least Error Boosting; LPBoosting:Linear Programming Boosting; NB:Naive Bayes; LightGBM:Light Gradient-Boosting Machine; MLP:Multilayer Perceptron; DNN:Deep Neural Networks; GB:Gradient Boosting; VFI:Voting Feature Intervals; GLMN:Generalized Linear Model Net; NLP:Natural Language Processing; LSTM:Long Short-Term Memory; SVC:Support Vector Classifier; BRF:Boosted Random Forest; ANN:Artificial Neural Networks; LASSO:Least Absolute Shrinkage and Selection Operator; PAR:Potentially Avoidable Readmissions; CMS:Centers for Medicare & Medicaid; PPR:Potentially Preventable Readmissions; SMOTE:Synthetic Minority Over-sampling Technique; KS-Test:Kolmogorov-Smirnov Test; PCA:Principal Component Analysis; RFE:Recursive Feature Elimination; AUC:Area Under the ROC curve; AUROC:Area Under Receiver Operating Characteristic Curve; PPV:Positive Predictive Values; NPV:Negative Predictive Values; SHAP:Shapley Additive Explanations; LR+:Positive Likelihood Ratio; NNT:Number Needed to Treat; PR-AUC:Precision Recall Aread under curve; MCC:Matthew's Correlation Coefficient; HF:Heart Failure; ECG:Electrocardiogram; BNP:Brain Natriuretic Peptide; KSUMC:King Saud University Medical City; NMMC:Northern Mindanao Medical Center; MARKER-HF:Machine Learning Assessment of Risk and Early Mortality in Heart Failure; UCI:University of California Irvine; UCSD:University of California San Diego; BIOSTAT-CHF:Biology Study to Tailored Treatment in Chronic Heart Failure; GWTG-HF:Get With The Guidelines-Heart Failure; ADHERE:Acute Decompensated Heart Failure National Registry; SHFM:Seattle Heart Failure Model; GISC:Gestione Integrata dello Scompenso Cardiaco

Article Snippet: Golas et al. [ ] , Partners Healthcare System , 28,031 , 30-day hospital readmission of HF patients , Modifiend deep learning, logistic regression, gradient boosting, neural network , Designing deep unified networks (DUNs) to avoid overfitting in predictive model , AUC, accuracy, precision, recall, f1 , LR=0.664, 0.626, 0.336, 0.616, 0.435; gradient boosting=0.650, 0.612, 0.325, 0.615, 0.425; maxout networks=0.695, 0.645, 0.354, 0.631, 0.454; DUN=0.705, 0.646, 0.360, 0.652, 0.464.

Techniques: Plasmid Preparation, Selection, Marker, Biomarker Discovery, Extraction, Comparison