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deep unified networks (duns)  (Partners HealthCare System Inc)

 
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    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
    https://www.bioz.com/product/deep+unified+networks+%28duns%29/pmc11652958-38-29-6?v=Partners+HealthCare+System+Inc
    Average 90 stars, based on 1 article reviews
    deep unified networks (duns) - by Bioz Stars, 2026-06
    90/100 stars

    Images

    1) Product Images from "Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review"

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

    Journal: Cureus

    doi: 10.7759/cureus.73876

    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
    Figure Legend 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

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



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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
    https://www.bioz.com/product/deep+unified+networks+%28duns%29/pmc11652958-38-29-6?v=Partners+HealthCare+System+Inc
    Average 90 stars, based on 1 article reviews
    deep unified networks (duns) - by Bioz Stars, 2026-06
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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