Application of a Neural Network for Gentamicin Concentration Prediction in a General Hospital Population

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Abstract
Neural network (NN) computation is computer modeling based in part on simulation of the structure and function of the brain. These modeling techniques have been found useful as pattern recognition tools. In the present study, data including age, sex, height, weight, serum creatinine concentration, dose, dosing interval, and time of measurement were collected from 240 patients with various diseases being treated with gentamicin in a general hospital setting. The patient records were randomly divided into two sets: a training set of 220 patients used to develop relationships between input and output variables (peak and trough plasma concentrations) and a testing set (blinded from the NN) of 20 to test the NN. The network model was the back-propagation, feed-forward model. Various networks were tested, and the most accurate networks for peak and trough (calculated as mean percent error, root mean squared error of the testing group, and r value between observed and predicted values) were reported. The results indicate that NNs can predict gentamicin serum concentrations accurately from various input data over a range of patient ages and renal function and may offer advantages over traditional dose prediction methods for gentamicin.