| 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 tau 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. |