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Table 4 Classification performance of prediction to differentiate toxicity concentrations (i.e., equal and above 1.5 ng/ml) or not, as compared to the observed serum digoxin concentrations, 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 parametersParametersTPTNFPFNRCP(%)SE(%)SP(%)
111All Variables1222479.32091.7
210-Sex2222382.84091.7
39-Sex-DCM3213282.86087.5
48-Sex-DCM -PH2222382.84091.7
57-Sex-DCM -PH -Captopril2186369.04075.0
66-Sex-DCM -PH -Captopril -Furosemide2204375.94083.3
75-Sex-DCM -PH -Captopril -Furosemide -VSD3168265.56066.7
84*-Sex-DCM -PH -Captopril -Furosemide -VSD -ibuprofen2213379.34087.5
  1. “- “in the column of parameters refers to “exclude” that specific variable from the model 1, which contain all variables. TP true positive (correctly classified to be ‘positive’); TN true negative (correctly classified to be ‘negative’); FP false positive (incorrectly classified to be ‘positive’); FN false negative (incorrectly classified to be ‘negative’), respectively; RCP rate of correct prediction; SE sensitivity; SP specificity
  2. All variables include: dose per total body weight, 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. *the common variables used in population pharmacokinetics were dose per total body weight, weight, PNA, CHF