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3 Biocybernetics and Biomedical Engineering

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Exploratory data analysis methods for comparison of drug dissolution profiles

Enăchescu C.

Biocybernetics and Biomedical Engineering

2010

Vol. 30, no. 4

17-27

A new approach for 'similarity' testing through comparison of drug products dissolution profiles, based on multivariate data analysis is presented. The dissolution curves corresponding to three products containing oxicams (piroxicam, meloxicam and tenoxicam) as oral solid dosage forms were obtained by dissolution tests at multiple pre-specified time points and in different compendial media. Dissolution data was simultaneously subjected to principal component and cluster analysis and comparisons between the dissolution characteristics of different products were carried out. All the results were compared with information provided by the difference (f1) and similarity (f2) factor tests. Unlike the f2 criterion, the proposed methods reflect variability within the individual dissolution curves, being also highly sensitive to profile variations.

Using multilayer neural networks to estimate the parameters of the nonlinear Baret model

Enăchescu C., Constantinescu A.

Biocybernetics and Biomedical Engineering

2007

Vol. 27, no. 3

95-111

The complexity of the nonlinear models with random parameters doesn't generally allow to resolve in an easy way the parameters estimation problem. In this paper we design and use a multilayer neural network (MLNN) for the parameters estimation. We deal with the Baret model for the temporal evolution of the leaf area index (LAI).

Evaluation of some statistical methods for referring women for bone densitometry

Enăchescu C.

Biocybernetics and Biomedical Engineering

2007

Vol. 27, no. 3

43-58

The aim of our study is to design and compare some predictive models for estimating the Bone Mineral Density score (BMD) t-score. The data were collected, except the t-score, by self-report from 356 women recruited from the Cantacuzino Hospital, Bucharest, Romania, during the period 1998-2003. The following methods are tuned and compared on this data: the General Regression Model (GRM), the Classification Trees (CT) and the Multilayer Perceptrons Network (MLP). Comparatively with a number of different bone densitometry criteria, currently used in clinical practice, we show that each of the above investigated models have a better sensitivity and specificity.