An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

ABSTRACT: We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). A previous population PK (PopPK) model of cyclosporin A was used as the comparison model. The patients' data were used for the ANN-PK model input, and the output by ANN was the clearance (CL). The estimated CL value from the ANN were substituted into the one-compartment with one-order absorption model, the concentrations were calculated, and the parameters of ANN were updated by the back-propagation method. Kernel SHAP was applied to the trained model and the SHAP value of each input was calculated. The root mean squared error for the PopPK mod

SUBMITTER: Ogami C 

PROVIDER: S-EPMC8302242 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature