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Stewart W. Wilson - One of the best experts on this subject based on the ideXlab platform.

  • function approximation with a classifier system
    Genetic and Evolutionary Computation Conference, 2001
    Co-Authors: Stewart W. Wilson
    Abstract:

    A classifier system, XCSF, is introduced in which the prediction estimation mechanism is used to learn approximations to functions. The Addition of Weight vectors to the classifiers allows piecewise-linear approximation. Results on functions of up to six dimensions show high accuracy. An interesting generalization of classifier structure is suggested.

Nicholas H. G. Holford - One of the best experts on this subject based on the ideXlab platform.

  • quantitative justification for target concentration intervention parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides
    British Journal of Clinical Pharmacology, 2004
    Co-Authors: Ivan Matthews, Carl M J Kirkpatrick, Nicholas H. G. Holford
    Abstract:

    Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The Addition of Weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Ivan Matthews - One of the best experts on this subject based on the ideXlab platform.

  • quantitative justification for target concentration intervention parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides
    British Journal of Clinical Pharmacology, 2004
    Co-Authors: Ivan Matthews, Carl M J Kirkpatrick, Nicholas H. G. Holford
    Abstract:

    Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The Addition of Weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Goran B Klintmalm - One of the best experts on this subject based on the ideXlab platform.

  • development and validation of gfr estimating equations using diabetes transplant and Weight
    Nephrology Dialysis Transplantation, 2010
    Co-Authors: Lesley A Stevens, Christopher H Schmid, Yaping L Zhang, Josef Coresh, Jane Manzi, Richard Landis, Omran Bakoush, Gabriel Contreras, Saul Genuth, Goran B Klintmalm
    Abstract:

    Background. We have reported a new equation (CKD-EPI equation) that reduces bias and improves accuracy for GFR estimation compared to the MDRD study equation while using the same four basic predictor variables: creatinine, age, sex and race. Here, we describe the development and validation of this equation as well as other equations that incorporate diabetes, transplant and Weight as Additional predictor variables. Methods. Linear regression was used to relate log-measured GFR (mGFR) to sex, race, diabetes, transplant, Weight, various transformations of creatinine and age with and without interactions. Equations were developed in a pooled database of 10 studies [2/3 (N = 5504) for development and 1/3 (N = 2750) for internal validation], and final model selection occurred in 16 Additional studies [external validation (N = 3896)]. Results. The mean mGFR was 68, 67 and 68 ml/min/ 1.73 m2 in the development, internal validation and external validation datasets, respectively. In external validation, an equation that included a linear age term and spline terms in creatinine to account for a reduction in the magnitude of the slope at low serum creatinine values exhibited the best performance (bias = 2.5, RMSE = 0.250) among models using the four basic predictor variables. Addition of terms for diabetes and transplant did not improve performance. Equations with Weight showed a small improvement in the subgroup with BMI <20 kg/m2. Conclusions. The CKD-EPI equation, based on creatinine, age, sex and race, has been validated and is more accurate than the MDRD study equation. The Addition of Weight, diabetes and transplant does not significantly improve equation performance.

Lynn Danford - One of the best experts on this subject based on the ideXlab platform.

  • is the impedance index ht2 r significant in predicting total body water
    The American Journal of Clinical Nutrition, 1992
    Co-Authors: Robert F Kushner, Dale A Schoeller, Carla R Fjeld, Lynn Danford
    Abstract:

    We investigated the general utility of bioelectrical impedance analysis (BIA) and the implications of BIA theory in populations of various ages from infancy to adulthood by developing a single impedance equation. Four subject data sets representing 62 adults, 37 prepubertal children, 44 preschool children, and 32 premature low-birth-Weight neonates were combined. Subjects were randomly divided into a development group (n = 116) and a cross-validation group (n = 59). The single best predictor of total body water (TBW) was height2/resistance (ht2/R), which explained 99% of the variation in TBW (SEE = 1.67 kg). The Addition of Weight reduced the SEE to 1.41 kg. A significant bias was only seen in the preschool children. These results were confirmed in the cross-validation group and the best prediction formula was TBW = 0.59 ht2/R + 0.065 wt + 0.04. We conclude that the impedance index (ht2/R) is a significant predictor of TBW and that there is some improvement in prediction of TBW by inclusion of a Weight term.