Článek popisuje možnosti zpracování medicínských dat potnoci programu Analyze. Program Analyze představuje propracovaný systém umožňující předzpracování a vizualizaci medicínských dat jak ve 2D, tak v 3D prostoru. v oblasti 2D jsou to nástroje pro konverzi vstupnich dat, filtraci (včetně rychlé Fourierovy a Wavelet transformace), segmentaci a operace s rastrovými obrazy. U prostorového zpracování je možné provádět rekonstrukce dat z paralelních rastrových řezů získaných například Z CT a MRI. Program zahrnuje nástroje pro měření a prezentaci výsledků.
The article deals with the possibilities of processing medical data using the program Analyze. The program Analyze represents a complex system for pre-processing and visualization of medical data in both 2D and 3D space. In 2D mode it represents image conversion, filtering (including fast Fourier and Wavelet transformation), segmentation and operations with raster pictures. In 3D it allows users to reconstruct data from parallel raster slices obtained from CT and MR. The program also comprises tools for measurement and presentation of results.
- MeSH
- Medical Informatics Computing MeSH
- Humans MeSH
- Software MeSH
- Data Display MeSH
- Check Tag
- Humans MeSH
Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D volume segmentations. To address the ongoing challenge of integrating and visualizing multimodal data, we developed Mol* Volumes and Segmentations (Mol*VS), which enables the interactive, web-based visualization of cellular imaging data supported by macromolecular data and biological annotations. Mol*VS is fully integrated into Mol* Viewer, which is already used for visualization by several public repositories. All EMDB and EMPIAR entries with segmentation datasets are accessible via Mol*VS, which supports the visualization of data from a wide range of electron and light microscopy experiments. Additionally, users can run a local instance of Mol*VS to visualize and share custom datasets in generic or application-specific formats including volumes in .ccp4, .mrc, and .map, and segmentations in EMDB-SFF .hff, Amira .am, iMod .mod, and Segger .seg. Mol*VS is open source and freely available at https://molstarvolseg.ncbr.muni.cz/.
Lecture notes in computer science ; 3337
xi, 508 stran
- MeSH
- Data Analysis MeSH
- Publication type
- Congress MeSH
- Collected Work MeSH
- Conspectus
- Biologické vědy
- NML Fields
- lékařská informatika
... Hallmarks of data quality in chemical exposure assessment: Introduction -- What do we mean by "data" ... ... -- From exposure data quality to the quality of exposure assessments -- Conclusions ... ... - 5.2.2 Fuzzy methods 48 -- 5.2.3 Probabilistic methods 49 -- 5.2.4 Sensitivity analysis 58 -- 5.3 Data ... ... WHAT DO WE MEAN BY “DATA” IN EXPOSURE ASSESSMENT? 145 -- 3. ... ... FROM EXPOSURE DATA QUALITY TO THE QUALITY OF EXPOSURE ASSESSMENTS 155 -- 5. CONCLUSIONS 157 -- 6. ...
IPCS harmonization project document ; no. 6
xiii, 158 s. : il., tab. ; 30 cm
- MeSH
- Risk Assessment MeSH
- Uncertainty MeSH
- Data Collection standards MeSH
- Environmental Exposure MeSH
- Conspectus
- Životní prostředí a jeho ochrana
- NML Fields
- environmentální vědy
- NML Publication type
- publikace WHO
Wiley series in probability and statistics
1st ed. xviii, 494 s.
Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.
- MeSH
- Humans MeSH
- Information Storage and Retrieval MeSH
- Cities MeSH
- Air Pollution * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Cities MeSH
Využití metod průzkumové analýzy dat (exploratory data analysis, EDA) je při hodnocení klinických dat v medicíně klíčovou fází. Vizualizační principy, modely poukazující na trendy vývoje či např. znázornění potenciálních závislostí, pomáhají k lepší interpretaci měření a v diagnostickém rozhodování. Počet dostupných moderních EDA balíků pro vývojáře v posledních letech roste v souvislosti s rozvojem oboru Data Science. NeuroEDA je interaktivní webová aplikace pro hodnocení biomedicínských dat. Aplikace byla naprogramována ve statistickém jazyce R, v rámci reaktivního paradigmatu frameworku Shiny. Je dále rozvíjena a využívána Katedrou biomedicínské informatiky FBMI ČVUT ve spolupráci s Neurologickou klinikou 1. LF UK a VFN v Praze, především pro hodnocení pacientů s dystoniemi a Parkinsonovou nemocí. Zpracování uživatelských dat v tabulkové formě (.csv, excel) probíhá v serverové části. Kromě základních popisných statistik, průzkumových grafů a shlukové analýzy, které jsou vhodné i pro hodnocení velkých dat, nabízí aplikace metody pro robustní a neparametrickou analýzu. Ty jsou v neurologii obzvlášť vhodné. Typicky z důvodu malých počtů a vlivných pozorování. Dále kvůli častému nesplnění dalších statistických předpokladů. Mezi její výhody patří snadná rozšiřitelnost o nové R balíky a rychlá odezva ve webových prohlížečích. Uživatelské interaktivní prostředí umožňuje práci s funkcemi jazyka R bez znalosti skriptování.
Usage of methods for exploratory data analysis (EDA) plays an important role in assessment of clinical medical data. Visualizations, models and illustration of dependency can help for better understanding of measurements in the diagnostics and making decision. The number of available modern EDA packages for developers is increasing as well as the development of the Data Science field. NeuroEDA is an interactive web application for biomedical data assessment. The application was programmed in the R statistical language, based on reactive paradigm framework called Shiny. It is further developed and used by the Department of Biomedical Informatics FBME CTU in cooperation with the Neurological Clinic of the 1st Medical Faculty, Charles University in Prague. Especially for the patients with dystonia and Parkinson's disease. Computations and processing of user data are executed on the server side. Basic descriptive statistics, interactive graphs, clustering, robust and LOESS regression were implemented and are suitable for analytics of big data. Robust regression is especially suitable in neurology. Typically, due to the small numbers of measured observations and failure to prove other statistical assumptions. Among its advantages we can consider easy expandability of new R packages and quick response in web browsers. User interface allows to work with the R language features without any scripting knowledge.
... Audience 2 -- References 2 -- Part 1: Overview of methods 3 -- Introduction 4 -- Key concepts and data ... ... to detect and monitor changes in essential health services 5 -- Step 2: Analysing and interpreting data ... ... 7 -- Step 3: Using data to inform action 15 -- References 15 -- Part 2: Programme-specific modules 16 ... ... changes to delivery and utilization of RMNCAH+N services 19 -- Step 2: Analysing and interpreting data ... ... the COVID-19 pandemic 39 -- Annex 3: RMNCAH+N indicator metadata 41 -- ANALYSING AND USING ROUTINE DATA ...
iv, 49 stran : ilustrace, grafy
- MeSH
- Betacoronavirus MeSH
- COVID-19 MeSH
- Data Management MeSH
- Disease Outbreaks MeSH
- Delivery of Health Care MeSH
- Data Collection MeSH
- Emergency Medical Services MeSH
- Health Services Needs and Demand MeSH
- Conspectus
- Veřejné zdraví a hygiena
- NML Fields
- veřejné zdravotnictví
- NML Publication type
- publikace WHO
