Prediction of tacrolimus blood levels by using the neural network with genetic algorithm in liver transplantation patients

The neural network (NN) is a technique using an artificial intelligent conc ept in predicting outcomes by using various input variables. Tacrolimus pha rmacokinetics has wide inter- and intra-subject variability and it is often difficult to predict its blood concentrations by dose alone. The objective s of this study are to select the clinically significant variables and to p redict the blood concentration of tacrolimus in liver transplant patients b y NN combined with genetic algorithm (GA). A total of thirty-two adult live r transplant patients from the University of Iowa Hospitals and Clinics wer e selected and the patients' data were retrospectively collected. These pat ient were randomly assigned into two groups: either the training group (n = 10), or testing group (n = 22). A back propagation (BP) NN was developed w hich con tained two hidden layers. A dynamic BP NN based on the time series concept was trained by using the current and previous data sets to predict the trough levels of tacrolimus. The mean of the NN prediction for tacroli mus blood levels was not significantly different from the observed value by a paired t-test comparison (12.05 +/- 2.67 ng/ml vs. 12.14 +/- 2.64 ng/ml, p = 0.80). The average difference of the testing sets between the observed and predicted levels was 1.74 ng/ml with a range from 0.08 to 5.26 ng/ml w hich is clinically acceptable range. Thirty-seven oat of 44 data sets (84%) in the testing group were within 3.0 ng/ml of the observed values. This st udy demonstrated that tacrolimus blood concentrations are precisely predict able in liver transplant patients using patients variables by NN.