Machine Learning Prediction of Heart Failure Readmissions

Figures

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  Central Illustration. A Machine Learning Model to Predict Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial Area under the receiver operating characteristic curve (AUC), eXtreme Gradient Boosting (XGB), least absolute shrinkage and selection operator (LASSO). Abbreviations as in Figures 2 and 3.

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  Figure 1. Receiver-Operating Curves for the eXtreme Gradient Boosting and Logistic Regression Event Prediction Model Delong's test was used to compare the AUC of the eXtreme Gradient Boosting model to the AUC of the logistic regression model. ROC = receiver operator characteristic.

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  Figure 2. Kaplan-Meier Curves for the eXtreme Gradient Boosting Model for the Prediction of Readmission With Heart Failure XGB Youden's J: 0.583, Optimal Threshold: 0.364 HF = heart failure.

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  Figure 3. SHAP Summary Plot Demonstrating Ranking of Feature Importance Based on SHAP Values for the Machine Learning Model (eXtreme Gradient Boosting) to Predict Heart Failure Representation in the RAPIDx AI Cohort Features are ranked in order of importance (from top to bottom) and are color coded by value (red indicating higher attribute values), with their horizontal location indicating their effect on the Shapley value (model output). CRP = C-reactive protein; COPD = chronic obstructive pulmonary disease; eGFR = estimated glomerular filtration rate.

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  Figure 4. Waterfall Plots for Individual Heart Failure Readmission Prediction (A and B) Beginning from the mean prediction at the bottom (Machine Learning score of 0.049), the waterfall plots display SHAP (SHapley Additive exPlanations) values in increasing order of magnitude, with red for increasing risk and blue for decreasing risk of heart failure representation. The positive or negative SHAP value of each feature is progressively added to reach the individual prediction. MDRD = modification of diet in renal disease; obbreviations as in Figures 2 and 3.