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XGBoost and SHAP modeling of clearance parameter for the Reference scenario; a. beeswarm plot of SHAP values and b. BorutaShap boxplot.

Journal: CPT: Pharmacometrics & Systems Pharmacology

Article Title: Stochastic Gates for Covariate Selection in Population Pharmacokinetics Modeling

doi: 10.1002/psp4.70147

Figure Lengend Snippet: XGBoost and SHAP modeling of clearance parameter for the Reference scenario; a. beeswarm plot of SHAP values and b. BorutaShap boxplot.

Article Snippet: In Ogami C et al. 2021 [ ], an artificial neural network (NN) was used to model individual random effects based on covariates, with SHAP (SHapley Additive exPlanations) [ ] values employed to explain the model. Janssen A et al. 2022 [ ] employed an XGBoost [ ] model instead of a NN.

Techniques: