AI Article Synopsis
- This study developed an artificial neural network (ANN) to predict blood concentrations of tacrolimus in liver transplant patients using 176 samples from 37 Chinese recipients.
- The ANN was optimized using a momentum method combined with a genetic algorithm and showed better performance compared to traditional multiple linear regression (MLR).
- Results indicated that 84.6% of the predictions by the ANN had an absolute error of less than 3.0 ng/mL, demonstrating good accuracy and precision in predicting tacrolimus levels.
Article Abstract
This study is to establish an artificial neural network (ANN) for predicting blood tacrolimus concentration in liver transplantation recipients. Tacrolimus concentration samples (176 samples) from 37 Chinese liver transplantation recipients were collected. ANN established after network parameters were optimized by using momentum method combined with genetic algorithm. Furthermore, the performance of ANN was compared with that of multiple linear regression (MLR). When using accumulated dose of 4 days before therapeutic drug monitoring (TDM) of tacrolimus concentration as input factor, mean prediction error and mean absolute prediction error of ANN were 0.02 +/- 2.40 ng x mL(-1) and 1.93 +/- 1.37 ng x mL(-1), respectively. The absolute prediction error of 84.6% of testing data sets was less than 3.0 ng x mL(-1). Accuracy and precision of ANN are superior to those of MLR. The correlation, accuracy and precision of ANN are good enough to predict blood tacrolimus concentration.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!