In this research study we have developed a clustering-based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all-night polysomnograph recordings. Past detection methods have been based on subject-independent and some subject-dependent parameters, such as fixed or variable thresholds to identify spindles. Using a multivariate Gaussian mixture model clustering technique, our algorithm was developed to use only subject-specific parameters to detect spindles. We have obtained an overall sensitivity range (65.1-74.1%) at a (59.55-119.7%) false positive proportion.

• MeSH
• algoritmy MeSH
• databáze faktografické * MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• multivariační analýza MeSH
• normální rozdělení MeSH
• polysomnografie metody   MeSH
• sběr dat metody   MeSH
• shluková analýza MeSH
• spánek fyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

We have developed a method to longitudinally classify subjects into two or more prognostic groups using longitudinally observed values of markers related to the prognosis. We assume the availability of a training data set where the subjects' allocation into the prognostic group is known. The proposed method proceeds in two steps as described earlier in the literature. First, multivariate linear mixed models are fitted in each prognostic group from the training data set to model the dependence of markers on time and on possibly other covariates. Second, fitted mixed models are used to develop a discrimination rule for future subjects. Our method improves upon existing approaches by relaxing the normality assumption of random effects in the underlying mixed models. Namely, we assume a heteroscedastic multivariate normal mixture for random effects. Inference is performed in the Bayesian framework using the Markov chain Monte Carlo methodology. Software has been written for the proposed method and it is freely available. The methodology is applied to data from the Dutch Primary Biliary Cirrhosis Study.

• MeSH
• biliární cirhóza farmakoterapie   MeSH
• biologické markery analýza   MeSH
• cholagoga a choleretika terapeutické užití   MeSH
• diskriminační analýza MeSH
• interpretace statistických dat MeSH
• kyselina ursodeoxycholová terapeutické užití   MeSH
• lidé MeSH
• lineární modely MeSH
• longitudinální studie MeSH
• počítačová simulace MeSH
• progrese nemoci MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Bacteria with a reduced susceptibility against antimicrobials pose a major threat to public health. Therefore, large programs have been set up to collect minimum inhibition concentration (MIC) values. These values can be used to monitor the distribution of the nonsusceptible isolates in the general population. Data are collected within several countries and over a number of years. In addition, the sampled bacterial isolates were not tested for susceptibility against one antimicrobial, but rather against an entire range of substances. Interest is therefore in the analysis of the joint distribution of MIC data on two or more antimicrobials, while accounting for a possible effect of covariates. In this regard, we present a Bayesian semiparametric density estimation routine, based on multivariate Gaussian mixtures. The mixing weights are allowed to depend on certain covariates, thereby allowing the user to detect certain changes over, for example, time. The new approach was applied to data collected in Europe in 2010, 2012, and 2013. We investigated the susceptibility of Escherichia coli isolates against ampicillin and trimethoprim, where we found that there seems to be a significant increase in the proportion of nonsusceptible isolates. In addition, a simulation study was carried out, showing the promising behavior of the proposed method in the field of antimicrobial resistance.

• MeSH
• antibakteriální látky farmakologie   MeSH
• bakteriální léková rezistence * MeSH
• Bayesova věta MeSH
• monitorování léčiv * MeSH
• multivariační analýza MeSH
• teoretické modely MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First, the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the mammogram with the search window. Then we evaluate the estimated mixture at each position and display the corresponding log-likelihood value as a gray level at the window center. The resulting log-likelihood image closely correlates with the structural details of the original mammogram and emphasizes unusual places. We assume that, in parallel use, the log-likelihood image may provide additional information to facilitate the identification of malignant lesions as atypical locations of high novelty.

• MeSH
• algoritmy MeSH
• diagnóza počítačová metody   MeSH
• financování organizované MeSH
• lidé MeSH
• mamografie MeSH
• multivariační analýza MeSH
• nádory prsu diagnóza   prevence a kontrola   MeSH
• normální rozdělení MeSH
• počítačové zpracování obrazu metody   MeSH
• statistické modely MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH

There is an emerging need in clinical research to accurately predict patients' disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a five-year follow-up period.

• MeSH
• algoritmy MeSH
• biologické markery * MeSH
• diskriminační analýza * MeSH
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• předškolní dítě MeSH
• progrese nemoci * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• matematické výpočty počítačové MeSH
• matematika MeSH
• numerická analýza pomocí počítače * MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Počítačová věda. Výpočetní technika. Informační technologie
• NLK Obory
• přírodní vědy
• přírodní vědy