A Population Model of Gentamicin Made with a New Nonparametric Em Algorithm

نویسنده

  • Roger W. Jelliffe
چکیده

A nonparametric EM (NPEM) algorithm for population pharmacokinetic modeling has been implemented as a computer program for the IBM PC and compatible machines. It computes the joint probability density function (PDF) for a 1 compartment pharmacokinetic model with intravenous dosing. It can operate using data of only one serum level per patient. The program utilizes patient data files from the USC*PACK PC clinical programs (1). Output includes a 3D plot of the joint PDF, two marginal PDF plots, means, variances, modes, quartiles, skewness, kurtosis, and covariance and correlation coefficient between parameters. Results can be entered into population files for use with the USC*PACK PC clinical programs. The first clinical study with this algorithm, of patients receiving intravenous gentamicin, is described. Results are compared with the standard 2-stage algorithm. Various parameterizations and sparse data sets are analyzed. The NPEM PC computer program permits population pharmacokinetic modeling in community hospitals.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nonparametric Estimation of Spatial Risk for a Mean Nonstationary Random Field}

The common methods for spatial risk estimation are investigated for a stationary random field. Because of simplifying, lets distribution is known, and parametric variogram for the random field are considered. In this paper, we study a nonparametric spatial method for spatial risk. In this method, we model the random field trend by a local linear estimator, and through bias-corrected residuals, ...

متن کامل

­­Image Segmentation using Gaussian Mixture Model

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

متن کامل

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

متن کامل

A New Nonparametric Regression for Longitudinal Data

In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...

متن کامل

Determination of the number of components in finite mixture distribution with Skew-t-Normal components

Abstract One of the main goal in the mixture distributions is to determine the number of components. There are different methods for determination the number of components, for example, Greedy-EM algorithm which is based on adding a new component to the model until satisfied the best number of components. The second method is based on maximum entropy and finally the third method is based on non...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1990