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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2008.1
- 수록면
- 147 - 159 (13page)
이용수
초록· 키워드
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Objectives: The primary objective of this study is to compare model performance of machine learning methods with that of a previous study in which a nonlinear mixed effects model was created using NONMEM? for the pharmacokinetic and pharmacodynamic data for propofol. The secondary objective was to evaluate if a pharmacodynamic model describing the relationship between the dose of propofol and bispectral index (BIS) outperform that describing the relationship between a pharmacokinetic model derived-predicted concentrations of propofol and BIS. Methods: Data were collected during a study involving the infusion of propofol into healthy volunteers. Pharmacokinetic and pharmacodynamic models were constructed using artificial neural networks (ANNs), support vector machines (SVMs), and multi-method ensembles and were compared with the nonlinear mixed effects method as implemented by NONMEM?. Model performance was assessed by goodnessoffit statistics, paired t-tests between predicted and observed values for each model and scatterplots. Results: In pharmacokinetic analysis, ensemble I, the mean of ANN and NONMEM? predictions, achieved minimal error and the highest correlation coefficient. SVM produced the highest error and the lowest correlation coefficient. In pharmacodynamic analysis, ANN exhibited the best performance. An ANN Model describing the relationship between the dose of propofol and BIS was not inferior to an ANN model describing the relationship between predicted concentrations of propofol derived from an ANN pharmacokinetic model and BIS. Conclusions: In pharmacokinetic analysis, ensemble combined with ANN achieved slightly better performance than NONMEM?. The relationship between the dose of propofol and BIS can be predicted without considering pharmacokinetics of propofol.
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등록된 정보가 없습니다.
참고문헌 (27)
참고문헌 신청
Arana E / 1999 / Validation procedures in radiologic diagnostic models. Neural network and logistic regression / Invest Radiol 34 (10) : 636 ~ 642
Bard JW / 2001 / The BIS monitor: a review and technology assessment / AAAN J 69 (6) : 477 ~ 483
Brier ME / 1995 / Neural network predicted peak and trough gentamicin concentrations / Pharm Res 12 (3) : 406 ~ 412
Burges C / / A Tutorial on Support Vector Machines for Pattern Recognition, In Data Mining and Knowledge discovery 2 / Kluwer Academic Publishers
Chow HH / 1997 / Application of neural networks to population pharmacokinetic data analysis / J Pharm Sci 86 (7) : 840 ~ 845
Bard JW / 2001 / The BIS monitor: a review and technology assessment / AAAN J 69 (6) : 477 ~ 483
Brier ME / 1995 / Neural network predicted peak and trough gentamicin concentrations / Pharm Res 12 (3) : 406 ~ 412
Burges C / / A Tutorial on Support Vector Machines for Pattern Recognition, In Data Mining and Knowledge discovery 2 / Kluwer Academic Publishers
Chow HH / 1997 / Application of neural networks to population pharmacokinetic data analysis / J Pharm Sci 86 (7) : 840 ~ 845
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