Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy

BACKGROUND: Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be “suitable” only when both “efficacy” and “safety” are considered. This study presents a model, namely the “ensemble strategy model,” to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 “suitable” and “unsuitable” patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman–Pearson, and Stacking were used for model training. The “ensemble strategy concept” was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens. RESULTS: The results of the tenfold cross-validation showed that the average accuracy of the proposed “ensemble strategy model” was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity. CONCLUSIONS: The “ensemble strategy model” can be used as a reference for the determination of vancomycin doses in clinical treatment.