xgboost decision tree algorithm Search Results


extreme gradient boosted decision tree (xgboost)  (Northwell Health Laboratories)
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Northwell Health Laboratories extreme gradient boosted decision tree (xgboost)
a ROC and PR curves with AUC and 95% CI for the retrospective ( n = 1889) and prospective ( n = 25,677; no updates) validation cohorts, b calibration plots for the retrospective validation cohort, c calibration plots for the prospective (no updates) validation cohort and d decision curves for the retrospective and prospective (no updates) cohorts based on the original NOCOS, logistic regression, and <t>XGBoost</t> models. The blue dots on the calibration plots show the actual proportion of outcomes averaged over deciles of the predicted probabilities. The red histograms show the counts of patients that survived past 28 days binned by the predicted probabilities. The green histograms show the counts of patients that died before 28 days binned by the predicted probabilities. The diagonal black lines indicate perfect calibration. The ICIs along with their 95% CIs are reported. ROC receiver operating characteristic, PR precision recall, AUC area under the ROC or PR curve, CI confidence interval, ICI integrated calibration index.
Extreme Gradient Boosted Decision Tree (Xgboost), supplied by Northwell Health Laboratories, 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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a ROC and PR curves with AUC and 95% CI for the retrospective ( n = 1889) and prospective ( n = 25,677; no updates) validation cohorts, b calibration plots for the retrospective validation cohort, c calibration plots for the prospective (no updates) validation cohort and d decision curves for the retrospective and prospective (no updates) cohorts based on the original NOCOS, logistic regression, and XGBoost models. The blue dots on the calibration plots show the actual proportion of outcomes averaged over deciles of the predicted probabilities. The red histograms show the counts of patients that survived past 28 days binned by the predicted probabilities. The green histograms show the counts of patients that died before 28 days binned by the predicted probabilities. The diagonal black lines indicate perfect calibration. The ICIs along with their 95% CIs are reported. ROC receiver operating characteristic, PR precision recall, AUC area under the ROC or PR curve, CI confidence interval, ICI integrated calibration index.

Journal: Nature Communications

Article Title: Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

doi: 10.1038/s41467-022-34646-2

Figure Lengend Snippet: a ROC and PR curves with AUC and 95% CI for the retrospective ( n = 1889) and prospective ( n = 25,677; no updates) validation cohorts, b calibration plots for the retrospective validation cohort, c calibration plots for the prospective (no updates) validation cohort and d decision curves for the retrospective and prospective (no updates) cohorts based on the original NOCOS, logistic regression, and XGBoost models. The blue dots on the calibration plots show the actual proportion of outcomes averaged over deciles of the predicted probabilities. The red histograms show the counts of patients that survived past 28 days binned by the predicted probabilities. The green histograms show the counts of patients that died before 28 days binned by the predicted probabilities. The diagonal black lines indicate perfect calibration. The ICIs along with their 95% CIs are reported. ROC receiver operating characteristic, PR precision recall, AUC area under the ROC or PR curve, CI confidence interval, ICI integrated calibration index.

Article Snippet: Three different model types—generalized linear model with the least absolute shrinkage and selection operator (LASSO) penalization (the Northwell COVID-19 Survival Calculator (NOCOS)) , logistic regression (LR) with LASSO penalization , and extreme gradient boosted decision tree (XGBoost) —were trained on patients from the development cohort for 28-day survival.

Techniques:

Discrimination (AUROC) and calibration (ICI) performance metrics in a 2000-patient sliding window with a step size of 500 patients for the original and dynamically updated 28-day a NOCOS, b logistic regression, and c XGBoost models. The updating methods are listed in the legend, and dynamic logistic regression is only applicable to the logistic regression model. Updates are performed when the ICI is greater than the threshold of 0.03. AUROC area under the receiver operating characteristic curve, ICI integrated calibration index, LR logistic regression.

Journal: Nature Communications

Article Title: Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

doi: 10.1038/s41467-022-34646-2

Figure Lengend Snippet: Discrimination (AUROC) and calibration (ICI) performance metrics in a 2000-patient sliding window with a step size of 500 patients for the original and dynamically updated 28-day a NOCOS, b logistic regression, and c XGBoost models. The updating methods are listed in the legend, and dynamic logistic regression is only applicable to the logistic regression model. Updates are performed when the ICI is greater than the threshold of 0.03. AUROC area under the receiver operating characteristic curve, ICI integrated calibration index, LR logistic regression.

Article Snippet: Three different model types—generalized linear model with the least absolute shrinkage and selection operator (LASSO) penalization (the Northwell COVID-19 Survival Calculator (NOCOS)) , logistic regression (LR) with LASSO penalization , and extreme gradient boosted decision tree (XGBoost) —were trained on patients from the development cohort for 28-day survival.

Techniques:

28-day performance metrics for the prospective ( n = 25,677) cohort

Journal: Nature Communications

Article Title: Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

doi: 10.1038/s41467-022-34646-2

Figure Lengend Snippet: 28-day performance metrics for the prospective ( n = 25,677) cohort

Article Snippet: Three different model types—generalized linear model with the least absolute shrinkage and selection operator (LASSO) penalization (the Northwell COVID-19 Survival Calculator (NOCOS)) , logistic regression (LR) with LASSO penalization , and extreme gradient boosted decision tree (XGBoost) —were trained on patients from the development cohort for 28-day survival.

Techniques:

a ROC and PR curves with AUC and 95% CI for the prospective ( n = 25,677) validation cohort, b calibration plots for the prospective validation cohort, and c decision curves for the prospective cohort based on NOCOS updated using logistic recalibration, logistic regression updated using logistic recalibration, and XGBoost updated using intercept only recalibration. The blue dots on the calibration plots show the actual proportion of outcomes averaged over deciles of the predicted probabilities. The red histograms show the counts of patients that survived past 28 days binned by the predicted probabilities. The green histograms show the counts of patients that died before 28 days binned by the predicted probabilities. The diagonal black lines indicate perfect calibration. The ICIs along with their 95% CIs are reported. ROC receiver operating characteristic, PR precision recall, AUC area under the ROC or PR curve, CI confidence interval, ICI integrated calibration index.

Journal: Nature Communications

Article Title: Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

doi: 10.1038/s41467-022-34646-2

Figure Lengend Snippet: a ROC and PR curves with AUC and 95% CI for the prospective ( n = 25,677) validation cohort, b calibration plots for the prospective validation cohort, and c decision curves for the prospective cohort based on NOCOS updated using logistic recalibration, logistic regression updated using logistic recalibration, and XGBoost updated using intercept only recalibration. The blue dots on the calibration plots show the actual proportion of outcomes averaged over deciles of the predicted probabilities. The red histograms show the counts of patients that survived past 28 days binned by the predicted probabilities. The green histograms show the counts of patients that died before 28 days binned by the predicted probabilities. The diagonal black lines indicate perfect calibration. The ICIs along with their 95% CIs are reported. ROC receiver operating characteristic, PR precision recall, AUC area under the ROC or PR curve, CI confidence interval, ICI integrated calibration index.

Article Snippet: Three different model types—generalized linear model with the least absolute shrinkage and selection operator (LASSO) penalization (the Northwell COVID-19 Survival Calculator (NOCOS)) , logistic regression (LR) with LASSO penalization , and extreme gradient boosted decision tree (XGBoost) —were trained on patients from the development cohort for 28-day survival.

Techniques:

a NOCOS, b logistic regression, and c XGBoost model predictor importances are plotted. The importance of the NOCOS and logistic regression model predictors are the coefficients of the linear predictor scaled by the standard deviations of the predictors from the development cohort. The importance of the XGBoost model coefficients is the weighted average over the ensemble of trees of the difference in node risk between the parent and children nodes due to splitting at each predictor.

Journal: Nature Communications

Article Title: Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

doi: 10.1038/s41467-022-34646-2

Figure Lengend Snippet: a NOCOS, b logistic regression, and c XGBoost model predictor importances are plotted. The importance of the NOCOS and logistic regression model predictors are the coefficients of the linear predictor scaled by the standard deviations of the predictors from the development cohort. The importance of the XGBoost model coefficients is the weighted average over the ensemble of trees of the difference in node risk between the parent and children nodes due to splitting at each predictor.

Article Snippet: Three different model types—generalized linear model with the least absolute shrinkage and selection operator (LASSO) penalization (the Northwell COVID-19 Survival Calculator (NOCOS)) , logistic regression (LR) with LASSO penalization , and extreme gradient boosted decision tree (XGBoost) —were trained on patients from the development cohort for 28-day survival.

Techniques: