Application of data mining to predict the dosage of vancomycin as an outcome variable in a teaching hospital population

International Journal of Clinical Pharmacology and Therapeutics

Volumn 44, Issue 11, 2006, Pages 533-538

  Chan, A L F ^a   Chen, J X ^a   Wang, H Y ^a  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

Author keywords

Decision tree method; Therapeutic drug monitoring; Vancomycin

Indexed keywords

CREATININE; METICILLIN; PENICILLIN G; VANCOMYCIN;

ADULT; AGE DISTRIBUTION; AGED; ANTIBIOTIC RESISTANCE; ANTIBIOTIC THERAPY; ARTICLE; BACTERIAL INFECTION; BODY WEIGHT; CLASSIFICATION; COMPARTMENT MODEL; CONTROLLED STUDY; CREATININE BLOOD LEVEL; DOSE CALCULATION; DOSE RESPONSE; DRUG BLOOD LEVEL; DRUG DISTRIBUTION; DRUG ELIMINATION; DRUG HALF LIFE; DRUG MONITORING; FEMALE; HUMAN; KIDNEY FUNCTION; LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS; PENICILLIN RESISTANCE; PREDICTION; SEX RATIO; STEADY STATE; TEACHING HOSPITAL;

EID: 33750861872     PISSN: 09461965     EISSN: None     Source Type: Journal    
DOI: 10.5414/CPP44533     Document Type: Article

References (14)

1. Barrett JS, Koprowski SP Jr. The epiphany of data warehousing technologies in the pharmaceutical industry. Int J Clin Pharmacol Ther. 2002; 40: S3-S13.
2. Clark P, Niblett T. The CN2 induction algorithm. Machine Learning. 1989; 3: 261-283.
3. Corrigan BW, Mayo PR, Jamali F. Application of a NN for gentamicin concentration prediction in a federal hospital population. Ther Drug Monit. 1997; 19: 25-28.
4. DeRouin E, Brown, J, Beck H, Fausett L, Schneider M. Neural network training on unequally represented classes. In: Dagli CH (ed). Intelligent engineering systems through artificial neural networks. New York: ASME Press; 1991. p 135-145.
5. Freund Y. Boosting a weak learning algorithm by majority. Information and Computation. 1996; 12: 256-285.
6. Freund Y, Schapire RE. Experiments with a new boosting algorithm. Proceedings of the Thirteenth National Conference on Machine Learning. 1996; 148-156.
7. Hart PE. The condensed nearest neighbor rule. IEEE Trans Inform Theory. 1968; IT-14: 515-516.
8. Koprowski SP Jr, Barrett JS. Data warehouse implementation with clinical pharmacokinetic/pharmacodynamic data. Int J Clin Pharmacol Ther. 2002; 40: S14-S29.
9. Lewis D, Catlett J. Heterogeneous uncertainty sampling for supervised learning. Proceedings of the 11th International Conference on Machine Learning. 1994; 144-156.
10. Lindquist M, Stahl M, Bate A et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Safety. 2000; 23: 533-542.
11. Quinlan JR. Induction of decision tree. Machine Learning. 1986; 1: 81-106.
12. Skalak D. Prototype and feature selection by sampling and random mutation hill climbing algorithms. Proceedings of 11th Machine Learning Conference. 1994; 293-301.
13. Smith BP, Brier M. Statistical approach to neural network model building for gentamicin peaks predictions. J Pharm Sci. 1996; 85: 65-69.
14. Witten IH, Frank E, Kaufmann M. Data mining: practical machine learning tools and technique with Jave implementations. San Francisco, CA: Morgan Kaufmann; 1999.