• MeSH
• Breast Neoplasms prevention & control   MeSH
• Mass Screening MeSH
• Socialism MeSH
• Geographicals
• Historical Geographic Locations MeSH

• MeSH
• Discriminant Analysis MeSH
• Air Pollutants analysis   MeSH
• Environmental Monitoring MeSH
• Data Collection MeSH
• Cluster Analysis MeSH
• Air Pollution MeSH
• Publication type
• Database MeSH

• MeSH
• Biometry MeSH
• Statistics as Topic MeSH

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika
• management, organizace a řízení zdravotnictví

• MeSH
• Multivariate Analysis MeSH

INTRODUCTION: Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals.

• MeSH
• Algorithms MeSH
• Principal Component Analysis MeSH
• Respiration MeSH
• Electroencephalography MeSH
• Electromyography MeSH
• Electrooculography MeSH
• Fourier Analysis MeSH
• Humans MeSH
• Markov Chains MeSH
• Brain growth & development   MeSH
• Infant, Newborn MeSH
• Signal Processing, Computer-Assisted MeSH
• Eye Movements MeSH
• Polysomnography MeSH
• Term Birth physiology   MeSH
• Reproducibility of Results MeSH
• Chi-Square Distribution MeSH
• Heart Rate MeSH
• Sleep Stages physiology   MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Infant, Newborn MeSH

• MeSH
• Survival MeSH
• Regression Analysis MeSH
• Research statistics & numerical data   MeSH

• Conspectus
• Kombinatorika. Teorie grafů. Matematická statistika. Operační výzkum. Matematické modelování
• NML Fields
• přírodní vědy
• NML Publication type
• kolektivní monografie

Množství dostupných dat, která jsou relevantní pro podporu klinického rozhodování, roste mnohem rychleji, než naše schopnost je analyzovat a interpretovat. Proto dosud není plně využit potenciál dat přispět ke stanovení správné diagnózy, terapie a prognózy jednotlivého pacienta. Měřená data mohou zajistit konkrétní přínos pro konkrétního pacienta, což však platí jen v případě, že jejich biostatistická analýza je provedena spolehlivě a pečlivě. To vyžaduje řešit výzvy, které se mohou jevit nesrozumitelnými pro nestatistiky. Cílem tohoto článku je diskutovat principy statistické analýzy velkých dat ve výzkumu i rutinních aplikacích v klinické medicíně, se zvláštním zřetelem na specifické aspekty psychiatrie. Biostatistická analýza dat ve speciálním oboru vyžaduje své specifické přístupy a odlišné zkušenosti oproti jiným klinickým oblastem, jak dokládají komplikace při analýze psychiatrických dat. Analýza velkých dat v psychiatrickém výzkumu i rutinních aplikacích je velmi vzdálena pouhé servisní činnosti využívající standardní metody mnohorozměrné statistiky a/nebo strojového učení.

The amount of available data relevant for clinical decision support is rising not only rapidly but at the same time much faster than our ability to analyze and interpret them. Thus, the potential of the data to contribute to determining the diagnosis, therapy and prognosis of an individual patient is not appropriately exploited. The hopes to obtain benefit from the data for an individual patient must be accompanied by a reliable and diligent biostatistical analysis which faces serious challenges not always clear to non-statisticians. The aim of this paper is to discuss principles of statistical analysis of big data in research and routine applications in clinical medicine, focusing on particular aspects of psychiatry. The paper brings arguments in favor of the idea that the biostatistical analysis of data in a specialty field requires different approaches and different experience compared to other clinical fields. This is illustrated by a description of common complications of the analysis of psychiatric data. Challenges of the analysis of big data in both psychiatric research and routine practice are explained, which are far from a routine service activity exploiting standard methods of multivariate statistics and/or machine learning. Important research questions, which are important in the current psychiatric research, are presented and discussed from the biostatistical point of view.

• MeSH
• Big Data MeSH
• Biostatistics MeSH
• Humans MeSH
• Neurosciences MeSH
• Psychiatry * statistics & numerical data   MeSH
• Data Collection MeSH
• Statistics as Topic MeSH
• Decision Support Systems, Clinical * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

• MeSH
• Ecology MeSH
• Multivariate Analysis MeSH

• Conspectus
• Veřejné zdraví a hygiena
• NML Fields
• environmentální vědy

The pharmaceutical industry has to tackle the explosion of high amounts of poorly soluble APIs. This phenomenon leads to numerous sophisticated solutions. These include the use of multifactorial data analysis identifying correlations between the components and dosage form properties, laboratory and production process parameters with respect to the API liberation Example of such API is bicalutamide. Improved liberation is achieved by particle size reduction. Laboratory batches, with different PSD of API, were filled into gelatinous capsules and consequently granulated for tablet compression. Comparative dissolution profiles with Casodex 150 mg (Astra Zeneca) were performed. The component analysis was used for the statistical evaluation of f1 and f2 factors and D(v,0.9) and D[4,3] parameters of PSD to identify optimal PSD values. Suitable PSD limits for API were statistically confirmed in laboratory and in commercial scale with respect to optimized tablet properties. The tablets were bioequivalent with originator (n = 20; 90% CI for ln AUC0-120: 99.8-111.9%; 90% CI for ln cmax: 101.1-112.9%). In conclusion, the micronisation of the API is still an efficient and inexpensive method improving the bioavailability, although there are more complicated and expensive methods available. Statistical multifactorial methods improved the safety and reproducibility of production.

• MeSH
• Anilides chemical synthesis   metabolism   MeSH
• Biological Availability MeSH
• Chemistry, Pharmaceutical methods   MeSH
• Multivariate Analysis MeSH
• Nitriles chemical synthesis   metabolism   MeSH
• Tablets MeSH
• Therapeutic Equivalency MeSH
• Tosyl Compounds chemical synthesis   metabolism   MeSH
• Publication type
• Journal Article MeSH

• Keywords
• Analýza dat, Analýza statistická, Regrese,

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika