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
- Depression * psychology MeSH
- Adult MeSH
- Epilepsy * psychology MeSH
- Qualitative Research * MeSH
- Middle Aged MeSH
- Humans MeSH
- Mobile Applications MeSH
- Wearable Electronic Devices MeSH
- Patient Preference psychology statistics & numerical data MeSH
- Multiple Sclerosis * psychology MeSH
- Aged MeSH
- Telemedicine MeSH
- Data Visualization MeSH
- Focus Groups * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. RESULTS: The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. CONCLUSIONS: The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website ( https://soykb.org/SoybeanAlleleCatalogTool/ ), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website ( https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana ). Researchers can use this tool to connect variant alleles of genes with meta-information of species.
- MeSH
- Alleles * MeSH
- Arabidopsis * genetics MeSH
- Data Mining * methods MeSH
- Datasets as Topic * MeSH
- Gene Frequency MeSH
- Genotype MeSH
- Glycine max * genetics MeSH
- Internet * MeSH
- Zea mays * genetics MeSH
- Metadata MeSH
- Mutation MeSH
- Pigmentation genetics MeSH
- Genes, Plant genetics MeSH
- Software * MeSH
- Amino Acid Substitution MeSH
- Plant Dormancy genetics MeSH
- Data Visualization MeSH
- Publication type
- Journal Article MeSH
Cílem článku je seznámit čtenáře s výhodami a nevýhodami sekvence 4D Flow. Vyšetření touto sekvencí umožňuje retrospektivně zjistit průtok a jiné parametry toku v objemu zájmu. Je ovšem náročné jak z hlediska času, tak následného zpracování dat. Pro vysokou cenu komerčních programů může být pro uživatele nutné vytvořit si vlastní nástroje zpracování dat. Komerční programy poskytují omezené nástroje segmentace, ale naopak zvládají všechny základní korekce a nabízí množství funkcionalit. Přes svůj velký potenciál má sekvence svá omezení, zejména je to nízké prostorové rozlišení a dlouhá doba akvizice.
The goal of this paper is to inform about the 4D Flow sequence, its advantages and disadvantages. 4D Flow examination allows to assess flow rate and other flow parameters in the volume of interest retrospectively. However, it is expensive in terms of time and postprocessing. An in-house software may be necessary, as commercial programs remain costly. They offer a number of functionalities and data corrections. Their segmentations tools, however, remain relatively limited. Low spatial resolution and long data acquisition are the primary limitations of the sequence
- Keywords
- 4D Flow,
- MeSH
- Electronic Data Processing * methods MeSH
- Diagnostic Techniques, Cardiovascular instrumentation MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Hemorheology MeSH
- Software MeSH
- Data Visualization MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- MeSH
- Data Analysis MeSH
- Survival Analysis MeSH
- Confidence Intervals MeSH
- Logistic Models MeSH
- Normal Distribution MeSH
- Risk MeSH
- ROC Curve MeSH
- Sensitivity and Specificity MeSH
- Statistical Distributions MeSH
- Models, Statistical MeSH
- Statistics as Topic * classification methods MeSH
- Data Visualization MeSH
During the time of the novel coronavirus disease 2019 (COVID-19) pandemic, it has been crucial to search for novel antiviral drugs from plants and well as other natural sources as alternatives for prophylaxis. This work reviews the antiviral potential of plant extracts, and the results of previous research for the treatment and prophylaxis of coronavirus disease and previous kinds of representative coronaviruses group. Detailed descriptions of medicinal herbs and crops based on their origin native area, plant parts used, and their antiviral potentials have been conducted. The possible role of plant-derived natural antiviral compounds for the development of plant-based drugs against coronavirus has been described. To identify useful scientific trends, VOSviewer visualization of presented scientific data analysis was used.
- MeSH
- Alkaloids chemistry pharmacology MeSH
- Antiviral Agents chemistry therapeutic use MeSH
- COVID-19 prevention & control MeSH
- COVID-19 Drug Treatment MeSH
- Flavonoids chemistry pharmacology MeSH
- Plants, Medicinal chemistry MeSH
- Humans MeSH
- Plant Extracts chemistry pharmacology therapeutic use MeSH
- Terpenes chemistry pharmacology MeSH
- Data Visualization MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- MeSH
- Communication MeSH
- Humans MeSH
- Oral Hygiene MeSH
- Persons with Hearing Disabilities * MeSH
- Data Visualization MeSH
- Sign Language MeSH
- Dental Hygienists MeSH
- Check Tag
- Humans MeSH
- Publication type
- Interview 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
- Electronic Data Processing methods MeSH
- Deep Learning * MeSH
- Electronic Health Records MeSH
- International Classification of Diseases * MeSH
- Neural Networks, Computer MeSH
- Machine Learning MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Data Visualization MeSH
- Natural Language Processing MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
První vydání 59 stran : barevné ilustrace ; 25 cm
Sborník prací přednesených na edukačním semináři, který informoval o metodách analýzy klinických a epidemiologických dat. Určeno odborné veřejnosti.
- MeSH
- Epidemiology MeSH
- Data Interpretation, Statistical MeSH
- Medical Oncology MeSH
- Statistics as Topic MeSH
- Information Storage and Retrieval MeSH
- Data Visualization MeSH
- Publication type
- Congress MeSH
- Collected Work MeSH
- Conspectus
- Lékařské vědy. Lékařství
- NML Fields
- statistika, zdravotnická statistika
- knihovnictví, informační věda a muzeologie
- NML Publication type
- semináře
Práce ukazuje, jakými současnými směry vývoje se ubírá v zobrazovacích postupech výpočetní tomografie. Jsou zmíněny možnosti snížení dávky pomocí cínové filtrace, skenovací možnosti hrudníku se separací pulmonální a aortální arteriální fáze, nízkodávková vyšetření dětí bez nutnosti sedace či anestezie, adaptivní aplikace kontrastní látky s redukcí jejího objemu, možnosti analýzy dat pomocí nových postupů, jako je pokročilá analýza materiálového složení, incipientní zavádění metod analýzy založené na hlubokém učení a umělé inteligenci.
The article is presentin current ways of the development of the scannng techniques in computed tomography. The following methods are mentioned: tin filtration of the spektra, scanning with the separate pulmonary and aortic arterial phase imaging, low-dose children imaging without sedation or anaesthesia, addaptive application of the contrast materiál with the possibilities of the dose reduction, novel trends in materiál analysis a the advent of the deep learning based methods and other artificial intelligence approaches in diagnostic.
