Name
Title | ||
|---|---|---|
Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis |
Abstract | ||
|---|---|---|
Drug-related problems, particularly those that result from sub- or overtherapeutic doses of high-alert medications, have become a growing concern in clinical medicine. In this paper, we use a model-tree-based regression technique (namely, M5) and support vector machine (SVM) for regression to develop learning-based systems for predicting the adequacy of a vancomycin regimen. We empirically evaluate each system's accuracy in predicting patients' peak and trough concentrations in different clinical scenarios characterized by renal functions and regimen types. Our data consist of 1099 clinical cases that were collected from a major tertiary medical center in southern Taiwan. We also examine the use of bagging for enhancing the prediction power of the respective systems and include in our evaluation a salient one-compartment model for performance benchmark purposes. Overall, our evaluation results suggest that both M5 and SVM are significantly more accurate than the benchmark one-compartment model in predicting patients' peak and trough concentrations across all investigated clinical scenarios. M5 appears to benefit considerably from bagging, which has a positive but seemingly smaller effect on SVM. Taken together, our findings indicate supervised learning techniques that are capable of effectively supporting clinicians' use of vancomycin or similar high-alert drugs in their patient care and management. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TSMCA.2007.897700 | IEEE Transactions on Systems, Man, and Cybernetics, Part A |
Keywords | Field | DocType |
pharmacokinetic data analysis,supervised learning approach,evaluation result,clinical case,benchmark one-compartment model,model-tree-based regression technique,high-alert medication,performance benchmark purpose,clinical use,clinical medicine,clinical scenario,different clinical scenario,trough concentration,high-alert drugs,support vector machines,bagging,compartment model,health care,accuracy,support vector machine,predictive models,technology management,data analysis,regression analysis,decision support systems,decision support,data consistency,data mining,renal function,supervised learning | Health care,Regimen,Data mining,Regression,Regression analysis,Computer science,Support vector machine,Decision support system,Supervised learning,Patient care,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 4 | 1083-4427 |
Citations | PageRank | References |
9 | 0.54 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. J.-H. Hu | 1 | 20 | 1.37 |
| Tsang-Hsiang Cheng | 2 | 141 | 12.02 |
| Chin-Ping Wei | 3 | 9 | 0.54 |
| Chun-Hui Yu | 4 | 9 | 0.54 |
| A. L.F. Chan | 5 | 9 | 0.54 |
| Hue-Yu Wang | 6 | 9 | 0.54 |