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Table 3 Area under the curve (AUC) of the receiver operating characteristic (ROC) curves to differentiate toxicity concentration (i.e., equal and above 1.5 ng/ml) or not for each ANN model on validation dataset

From: Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network

ModelNo. of parametersParametersAUCSESig.95% CI
Lower BondUpper Bond
111All Variables0.5580.1510.6860.2630.854
210-Sex0.6580.1520.2730.3610.956
39-Sex-DCM0.7380.1400.1000.4631.000
48-Sex-DCM -PH0.6580.1520.2730.3610.956
57-Sex-DCM -PH -Captopril0.5750.1470.6030.2860.864
66-Sex-DCM -PH -Captopril -Furosemide0.6170.1500.4190.3240.910
75-Sex-DCM -PH -Captopril -Furosemide -VSD0.6330.1410.3560.3570.909
84*-Sex-DCM -PH -Captopril -Furosemide -VSD -ibuprofen0.6380.1510.3410.3420.933
  1. “- “in the column of parameters refers to “exclude” that specific variable from the model 1, which contain all variables. AUC area under the curve; SE standard error of AUC; Sig. significance of AUC finding
  2. All 11 variables include: dose per total body weight, gender, postmenstrual age (PMA), Congestive heart failure (CHF), dilated cardiomyopathy (DCM), pulmonary hypertension (PH), Ventricular septal defect (VSD), with captopril, with furosemide, with ibuprofen
  3. *Common variables used in population pharmacokinetics were dose per total body weight, PMA, CHF