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
• lékařská počítačová informatika MeSH
• lidé MeSH
• software MeSH
• zobrazování dat MeSH
• Check Tag
• lidé MeSH

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
• lidé MeSH
• ukládání a vyhledávání informací MeSH
• velkoměsta MeSH
• znečištění ovzduší * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• velkoměsta MeSH

BACKGROUND: Remote measurement technology (RMT) involves the use of wearable devices and smartphone apps to measure health outcomes in everyday life. RMT with feedback in the form of data visual representations can facilitate self-management of chronic health conditions, promote health care engagement, and present opportunities for intervention. Studies to date focus broadly on multiple dimensions of service users' design preferences and RMT user experiences (eg, health variables of perceived importance and perceived quality of medical advice provided) as opposed to data visualization preferences. OBJECTIVE: This study aims to explore data visualization preferences and priorities in RMT, with individuals living with depression, those with epilepsy, and those with multiple sclerosis (MS). METHODS: A triangulated qualitative study comparing and thematically synthesizing focus group discussions with user reviews of existing self-management apps and a systematic review of RMT data visualization preferences. A total of 45 people participated in 6 focus groups across the 3 health conditions (depression, n=17; epilepsy, n=11; and MS, n=17). RESULTS: Thematic analysis validated a major theme around design preferences and recommendations and identified a further four minor themes: (1) data reporting, (2) impact of visualization, (3) moderators of visualization preferences, and (4) system-related factors and features. CONCLUSIONS: When used effectively, data visualizations are valuable, engaging components of RMT. Easy to use and intuitive data visualization design was lauded by individuals with neurological and psychiatric conditions. Apps design needs to consider the unique requirements of service users. Overall, this study offers RMT developers a comprehensive outline of the data visualization preferences of individuals living with depression, epilepsy, and MS.

• MeSH
• deprese * psychologie   MeSH
• dospělí MeSH
• epilepsie * psychologie   MeSH
• kvalitativní výzkum * MeSH
• lidé středního věku MeSH
• lidé MeSH
• mobilní aplikace MeSH
• nositelná elektronika MeSH
• pacientova volba psychologie   statistika a číselné údaje   MeSH
• roztroušená skleróza * psychologie   MeSH
• senioři MeSH
• telemedicína MeSH
• vizualizace dat MeSH
• zjišťování skupinových postojů * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The knowledge of cancer burden in the population, its time trends, and the possibility of international comparison is an important starting point for cancer programs. A reliable interactive tool describing cancer epidemiology in children and adolescents has been nonexistent in the Czech Republic until recently. OBJECTIVE: The goal of this study is to develop a new web portal entitled the Czech Childhood Cancer Information System (CCCIS), which would provide information on childhood cancer epidemiology in the Czech Republic. METHODS: Data on childhood cancers have been obtained from the Czech National Cancer Registry. These data were validated using the clinical database of childhood cancer patients and subsequently combined with data from the National Register of Hospitalised Patients and with data from death certificates. These validated data were then used to determine the incidence and survival rates of childhood cancer patients aged 0 to 19 years who were diagnosed in the period 1994 to 2016 (N=9435). Data from death certificates were used to monitor long-term mortality trends. The technical solution is based on the robust PHP development Symfony framework, with the PostgreSQL system used to accommodate the data basis. RESULTS: The web portal has been available for anyone since November 2019, providing basic information for experts (ie, analyses and publications) on individual diagnostic groups of childhood cancers. It involves an interactive tool for analytical reporting, which provides information on the following basic topics in the form of graphs or tables: incidence, mortality, and overall survival. Feedback was obtained and the accuracy of outputs published on the CCCIS portal was verified using the following methods: the validation of the theoretical background and the user testing. CONCLUSIONS: We developed software capable of processing data from multiple sources, which is freely available to all users and makes it possible to carry out automated analyses even for users without mathematical background; a simple selection of a topic to be analyzed is required from the user.

• MeSH
• analýza dat * MeSH
• dítě MeSH
• incidence MeSH
• informační systémy MeSH
• lidé MeSH
• mladiství MeSH
• nádory * epidemiologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. OBJECTIVE: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. METHODS: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. RESULTS: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. CONCLUSIONS: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Background: Classifying diseases into ICD codes has mainly relied on human reading a large amount of written materials, such as discharge diagnoses, chief complaints, medical history, and operation records as the basis for classification. Coding is both laborious and time consuming because a disease coder with professional abilities takes about 20 minutes per case in average. Therefore, an automatic code classification system can significantly reduce the human effort. Objectives: This paper aims at constructing a machine learning model for ICD-10 coding, where the model is to automatically determine the corresponding diagnosis codes solely based on free-text medical notes. Methods: In this paper, we apply Natural Language Processing (NLP) and Recurrent Neural Network (RNN) architecture to classify ICD-10 codes from natural language texts with supervised learning. Results: In the experiments on large hospital data, our predicting result can reach F1-score of 0.62 on ICD-10-CM code. Conclusion: The developed model can significantly reduce manpower in coding time compared with a professional coder.

• MeSH
• automatizované zpracování dat metody   MeSH
• deep learning * MeSH
• elektronické zdravotní záznamy MeSH
• mezinárodní klasifikace nemocí * MeSH
• neuronové sítě MeSH
• strojové učení MeSH
• ukládání a vyhledávání informací metody   statistika a číselné údaje   MeSH
• vizualizace dat MeSH
• zpracování přirozeného jazyka MeSH
• Publikační typ
• práce podpořená grantem MeSH

Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ4 band and low β1 band) demonstrated significant negative linear relationship (pFWE < 0.05) to the frequent stimulus and three patterns (two low δ2 and δ3 bands, and narrow θ1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ4, β1, and θ1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.

• Publikační typ
• časopisecké články MeSH

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.

• MeSH
• algoritmy MeSH
• databáze faktografické MeSH
• databáze genetické MeSH
• genomika MeSH
• internet MeSH
• lékařská onkologie MeSH
• lidé MeSH
• nádory genetika   MeSH
• počítačová grafika MeSH
• proteomika MeSH
• průběh práce MeSH
• software MeSH
• transkriptom MeSH
• uživatelské rozhraní počítače MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Figurální (neboli neverbální) fluence je schopnost exekutivních funkcí, která nám poskytuje informace o divergentním myšlení, rozdělené pozornosti, plánování a mentální flexibilitě. Zhoršený výkon v testech zaměřených na figurální fluenci nacházíme u pacientů s neurologickým i psychiatrickým postižením. Pětitečkový test (Five Point Test, 5TT) je jeden z neuropsychologických testů, jenž slouží ke zhodnocení figurální fluence. Úkolem probanda je vytvořit co nejvíce obrazců v časovém limitu. Cílem této studie bylo vytvořit normy k 5TT pro českou dospělou populaci. Předkládáme normativní data pro dospělé ve věku od 20 do 85 let (n = 503). Hodnotili jsme počet správných odpovědí a počet perseverací. Počet správných odpovědí je ovlivněn věkem a vzděláním (r = –0,3; resp. 0,4; p < 0,0001), proto jsou normy rozděleny na pásma po 10 letech a dále podle ukončeného vzdělání. Počet perseverací s těmito proměnnými souvisí jen slabě (rs = 0,1; resp. –0,1; p < 0,05). Pohlaví nemá vliv na počet správných odpovědí ani perseverací (t = 0,09; p > 0,9 pro oba skóry).

Figural (or nonverbal) fluency is the ability of executive functions to provide information about divergent reasoning, divided attention, planning and mental flexibility. Impairments of figural fluency have been found in individuals with various neurological or psychiatric diseases. Five Point Test (5TT) is a neuropsychological test that assesses figural fluency. A participant is asked to generate as many unique designs as possible in a certain time limit. The aim of this study was to create Czech population norms for the Five Point Test. Normative data for adult population aged between 20 and 85 years (n = 503) are presented. We assessed the number of correct answers and the number of perseverations. The number of correct answers is influenced by age and education (r = –0.3 and 0.4, respectively, p < 0.0001); for this reason the norms are stratified into ten age ranges and also according to completed education. The number of perseverations correlates with these variables only weakly (rs = 0.1 and –0.1, respectively, p < 0.05). Gender has no impact neither on the number of correct answers nor on perseverative responses (t-test, p > 0.9 for both scores). Key words: Five Point Test – design fluency – normative data – executive functions – validity The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers.

• MeSH
• dospělí MeSH
• exekutivní funkce * MeSH
• kognitivní poruchy * diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• neuropsychologické testy * normy   statistika a číselné údaje   MeSH
• psychomotorický výkon MeSH
• referenční hodnoty MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• statistika jako téma MeSH
• stupeň vzdělání MeSH
• věkové faktory MeSH
• věkové rozložení MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• práce podpořená grantem MeSH
• validační studie MeSH

• MeSH
• hodnocení rizik MeSH
• nejistota MeSH
• sběr dat normy   MeSH
• vystavení vlivu životního prostředí MeSH

• Konspekt
• Životní prostředí a jeho ochrana
• NLK Obory
• environmentální vědy
• NLK Publikační typ
• publikace WHO