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Ronaldson, Amy, Evangelos Chandakas, Qiongwen Kang, Katie Brennan, Aminat Akande, Irene Ebyarimpa, Eleanor Wyllie, et al. "Cohort profile: he East London Health and Care Partnership Data Repository: using novel integrated data to support commissioning and research." BMJ Open 10, no. 9 (September 2020): e037183. http://dx.doi.org/10.1136/bmjopen-2020-037183.
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PurposeThe East London Health and Care Partnership (ELHCP) Data Repository was established to support commissioning decisions in London. This dataset comprises routine clinical data for the general practitioner (GP)-registered populations of two London boroughs, Tower Hamlets and City and Hackney, and provides a rich source of demographic, clinical and health service use data of relevance to clinicians, commissioners, researchers and policy makers. This paper describes the dataset in its current form, its representativeness and data completeness.ParticipantsThere were 351 749 and 344 511 members of the GP-registered population in the two boroughs, respectively, for the financial year 2017/2018. Demographic information and prevalence data were available for 9 mental health and 15 physical health conditions. Prevalence rates from the cohort were compared with local and national data. In order to illustrate the health service use data available in the dataset, emergency department use across mental health conditions was described. Information about data completeness was provided.Findings to dateThe ELHCP Data Repository provides a rich source of information about a relatively young, urban, ethnically diverse, population within areas of socioeconomic deprivation. Prevalence data were in line with local and national statistics with some exceptions. Physical health conditions were more common in those with mental health conditions, reflecting that comorbidities are the norm rather than the exception. This has implications for integrated care. Data completeness for risk factors (eg, blood pressure, cholesterol) was high in patients with long-term conditions.Future plansThe data are being further cleaned and evaluated using imputation, Bayesian and economic methods, principally focusing on specific cohorts, including type II diabetes, depression and personality disorder. Data continue to be collected for the foreseeable future to support commissioning decisions, which will also enable more long-term prospective analysis as data become available at the end of each financial year.
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Kapsner, Lorenz A., Jonathan M. Mang, Sebastian Mate, Susanne A. Seuchter, Abishaa Vengadeswaran, Franziska Bathelt, Noemi Deppenwiese, Dennis Kadioglu, Detlef Kraska, and Hans-Ulrich Prokosch. "Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository." Applied Clinical Informatics 12, no. 04 (August 2021): 826–35. http://dx.doi.org/10.1055/s-0041-1733847.
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Abstract Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites. Objectives Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats. Conclusion The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.
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Juárez, D., E. E. Schmidt, S. Stahl-Toyota, F. Ückert, and M. Lablans. "A Generic Method and Implementation to Evaluate and Improve Data Quality in Distributed Research Networks." Methods of Information in Medicine 58, no. 02/03 (September 2019): 086–93. http://dx.doi.org/10.1055/s-0039-1693685.
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Abstract Background With the increasing personalization of clinical therapies, translational research is evermore dependent on multisite research cooperations to obtain sufficient data and biomaterial. Distributed research networks rely on the availability of high-quality data stored in local databases operated by their member institutions. However, reusing data documented by independent health providers for the purpose of care, rather than research (“secondary use”), reveal a high variability in terms of data formats, as well as poor data quality, across network sites. Objectives The aim of this work is the provision of a process for the assessment of data quality with regard to completeness and syntactic accuracy across independently operated data warehouses using common definitions stored in a central (network-wide) metadata repository (MDR). Methods For assessment of data quality across multiple sites, we employ a framework of so-called bridgeheads. These are federated data warehouses, which allow the sites to participate in a research network. A central MDR is used to store the definitions of the commonly agreed data elements and their permissible values. Results We present the design for a generator of quality reports within a bridgehead, allowing the validation of data in the local data warehouse against a research network's central MDR. A standardized quality report can be produced at each network site, providing a means to compare data quality across sites, as well as to channel feedback to the local data source systems, and local documentation personnel. A reference implementation for this concept has been successfully utilized at 10 sites across the German Cancer Consortium. Conclusions We have shown that comparable data quality assessment across different partners of a distributed research network is feasible when a central metadata repository is combined with locally installed assessment processes. To achieve this, we designed a quality report and the process for generating such a report. The final step was the implementation in a German research network.
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Do Viet Duc, Ngo Thanh Long, Ha Trung Hai, Chu Van Hai, and Nghiem Van Tam. "A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 3–15. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.3-15.
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Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propose a hybrid method encompassing PFCM and improved Cuckoo search to form the proposed PFCM-ICS. The proposed method has been evaluated on 4 data sets issued from the UCI Machine Learning Repository and compared with recent clustering algorithms such as FCM, PFCM, PFCM based on particle swarm optimization (PSO), PFCM based on CS. Experimental results show that the proposed method gives better clustering quality and higher accuracy than other algorithms.
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Mate, Sebastian, Marvin Kampf, Wolfgang Rödle, Stefan Kraus, Rumyana Proynova, Kaisa Silander, Lars Ebert, et al. "Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC." Applied Clinical Informatics 10, no. 04 (August 2019): 679–92. http://dx.doi.org/10.1055/s-0039-1695793.
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Abstract Background High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. Objectives To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. Methods Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. Results The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. Conclusion A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.
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Paris, Nicolas, Antoine Lamer, and Adrien Parrot. "Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study." JMIR Medical Informatics 9, no. 12 (December 14, 2021): e30970. http://dx.doi.org/10.2196/30970.
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Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
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Paris, Nicolas, Antoine Lamer, and Adrien Parrot. "Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study." JMIR Medical Informatics 9, no. 12 (December 14, 2021): e30970. http://dx.doi.org/10.2196/30970.
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Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
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Beecham, Roger, Jason Dykes, Layik Hama, and Nik Lomax. "On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases." ISPRS International Journal of Geo-Information 10, no. 4 (April 1, 2021): 213. http://dx.doi.org/10.3390/ijgi10040213.
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Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic.
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Nkosi, Vusumuzi, Angela Mathee, Suzana Blesic, Thandi Kapwata, Zamantimande Kunene, David Jean du Preez, Rebecca Garland, and Caradee Yael Wright. "Exploring Meteorological Conditions and Human Health Impacts during Two Dust Storm Events in Northern Cape Province, South Africa: Findings and Lessons Learnt." Atmosphere 13, no. 3 (March 5, 2022): 424. http://dx.doi.org/10.3390/atmos13030424.
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Dust storms are meteorological hazards associated with several adverse health impacts including eye irritations, respiratory and cardiovascular disorders, and vehicular road accidents due to poor visibility. This study investigated relations between admissions from a large, public hospital that serves people living in Northern Cape and Free State provinces, South Africa during 2011 to 2017, and meteorological variables (temperature and air quality) during two dust storms, one in October 2014 (spring) and the second in January 2016 (summer), identified from the media as no repository of such events exists for South Africa. Distributed nonlinear lag analysis and wavelet transform analysis were applied to explore the relationships between hospital admissions for respiratory and cardiovascular diseases, eye irritation, and motor vehicle accidents; maximum temperature, and two air quality ‘proxy measures,’ aerosol optical depth and Ångström exponent, were used as ground-based air quality data were unavailable. Eye irritation was the most common dust-related hospital admission after both dust storm events. No statistically significant changes in admissions of interest occurred at the time of the two dust storm events, using either of the statistical methods. Several lessons were learnt. For this type of study, ground-based air quality and local wind data are required; alternative statistical methods of analysis should be considered; and a central dust storm repository would help analyze more than two events. Future studies in South Africa are needed to develop a baseline for comparison of future dust storm events and their impacts on human health.
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Sampurno, Fanny, Ashwini Kannan, Mark Lucas, John Liman, Sarah E. Connor, Emily Pearman, Jeremy L. Millar, et al. "Development of Technologic Solutions to Address Complex Local Requirements of an International Prostate Cancer Clinical Quality Registry." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–11. http://dx.doi.org/10.1200/cci.18.00114.
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PURPOSE To detail the process for importing a defined data set into a centralized global registry via a secure file transfer platform and to understand the barriers to the establishment of a centralized global registry. RESULTS A bespoke solution was developed to allow transmission of data from international local data centers to a centralized repository. Data elements included in the import template were drawn from existing International Consortium for Health Outcome Measurement variables and refined to ensure accurate benchmarking as well as feasibility in data completeness. The data set was organized in accordance with the prostate cancer care trajectory. Key considerations in developing the data transfer platform included import file format, process of input validation, and technical provisions. Given the diversity in the legislation and ethical requirements with respect to consent, data handling, and cross-border data transfer across geographic locations, we encouraged each local data center to consult with its legal advisors and research ethics committee early on in the process. DISCUSSION A global collaboration, although highly valuable, posed many challenges because of inconsistent methods of data collection. User acceptance of a system is paramount to the success of establishing a metaregistry. Local information technology support and regular regression testing ensures quality and maintenance of the database. CONCLUSION We developed a Web-based system to facilitate the collection and secure storage of common data, which is scalable and secure. It is anticipated that through systematic recording of data, global standards of clinical practice and outcomes of care will see vast improvements.
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Patel, Ashokkumar A., John R. Gilbertson, Louise C. Showe, Jack W. London, Eric Ross, Michael F. Ochs, Joseph Carver, et al. "A Novel Cross-Disciplinary Multi-Institute Approach to Translational Cancer Research: Lessons Learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)." Cancer Informatics 3 (January 2007): 117693510700300. http://dx.doi.org/10.1177/117693510700300002.
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Background The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu ) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium's intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers. Methods The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission. The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards. Results Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also developed a central web-based tool that allows investigators to share their translational (genomics/proteomics) experiment data on research evaluating potential biomarkers via a central location on the Consortium's web site. Conclusions The technological achievements and the statewide informatics infrastructure that have been established by the Consortium will enable robust and efficient studies of biomarkers and their relevance to the clinical course of cancer.
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Mamede, Rafael, Pedro Vila-Cerqueira, Mickael Silva, João A. Carriço, and Mário Ramirez. "Chewie Nomenclature Server (chewie-NS): a deployable nomenclature server for easy sharing of core and whole genome MLST schemas." Nucleic Acids Research 49, no. D1 (October 17, 2020): D660—D666. http://dx.doi.org/10.1093/nar/gkaa889.
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Abstract Chewie Nomenclature Server (chewie-NS, https://chewbbaca.online/) allows users to share genome-based gene-by-gene typing schemas and to maintain a common nomenclature, simplifying the comparison of results. The combination between local analyses and a public repository of allelic data strikes a balance between potential confidentiality issues and the need to compare results. The possibility of deploying private instances of chewie-NS facilitates the creation of nomenclature servers with a restricted user base to allow compliance with the strictest data policies. Chewie-NS allows users to easily share their own schemas and to explore publicly available schemas, including informative statistics on schemas and loci presented in interactive charts and tables. Users can retrieve all the information necessary to run a schema locally or all the alleles identified at a particular locus. The integration with the chewBBACA suite enables users to directly upload new schemas to chewie-NS, download existing schemas and synchronize local and remote schemas from chewBBACA command line version, allowing an easier integration into high-throughput analysis pipelines. The same REST API linking chewie-NS and the chewBBACA suite supports the interaction of other interfaces or pipelines with the databases available at chewie-NS, facilitating the reusability of the stored data.
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Morris, Joan K., Ester Garne, Maria Loane, Ingeborg Barisic, James Densem, Anna Latos-Bieleńska, Amanda Neville, et al. "EUROlinkCAT protocol for a European population-based data linkage study investigating the survival, morbidity and education of children with congenital anomalies." BMJ Open 11, no. 6 (June 2021): e047859. http://dx.doi.org/10.1136/bmjopen-2020-047859.
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IntroductionCongenital anomalies (CAs) are a major cause of infant mortality, childhood morbidity and long-term disability. Over 130 000 children born in Europe every year will have a CA. This paper describes the EUROlinkCAT study, which is investigating the health and educational outcomes of children with CAs for the first 10 years of their lives.Methods and analysisEUROCAT is a European network of population-based registries for the epidemiological surveillance of CAs. EUROlinkCAT is using the EUROCAT infrastructure to support 22 EUROCAT registries in 14 countries to link their data on births with CAs to mortality, hospital discharge, prescription and educational databases. Once linked, each registry transforms their case data into a common data model (CDM) format and they are then supplied with common STATA syntax scripts to analyse their data. The resulting aggregate tables and analysis results are submitted to a central results repository (CRR) and meta-analyses are performed to summarise the results across all registries. The CRR currently contains data on 155 594 children with a CA followed up to age 10 from a population of 6 million births from 1995 to 2014.EthicsThe CA registries have the required ethics permissions for routine surveillance and transmission of anonymised data to the EUROCAT central database. Each registry is responsible for applying for and obtaining additional ethics and other permissions required for their participation in EUROlinkCAT.DisseminationThe CDM and associated documentation, including linkage and standardisation procedures, will be available post-EUROlinkCAT thus facilitating future local, national and European-level analyses to improve healthcare. Recommendations to improve the accuracy of routinely collected data will be made.Findings will provide evidence to inform parents, health professionals, public health authorities and national treatment guidelines to optimise diagnosis, prevention and treatment for these children with a view to reducing health inequalities in Europe.
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Rattray, Nicholas A., Teresa M. Damush, Edward J. Miech, Barbara Homoya, Laura J. Myers, Lauren S. Penney, Jared Ferguson, Brenna Giacherio, Meetesh Kumar, and Dawn M. Bravata. "Empowering Implementation Teams with a Learning Health System Approach: Leveraging Data to Improve Quality of Care for Transient Ischemic Attack." Journal of General Internal Medicine 35, S2 (September 1, 2020): 823–31. http://dx.doi.org/10.1007/s11606-020-06160-y.
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Abstract Background Questions persist about how learning healthcare systems should integrate audit and feedback (A&F) into quality improvement (QI) projects to support clinical teams’ use of performance data to improve care quality. Objective To identify how a virtual “Hub” dashboard that provided performance data for patients with transient ischemic attack (TIA), a resource library, and a forum for sharing QI plans and tools supported QI activities among newly formed multidisciplinary clinical teams at six Department of Veterans Affairs (VA) medical centers. Design An observational, qualitative evaluation of how team members used a web-based Hub. Participants External facilitators and multidisciplinary team members at VA facilities engaged in QI to improve the quality of TIA care. Approach Qualitative implementation process and summative evaluation of observational Hub data (interviews with Hub users, structured field notes) to identify emergent, contextual themes and patterns of Hub usage. Key Results The Hub supported newly formed multidisciplinary teams in implementing QI plans in three main ways: as an information interface for integrated monitoring of TIA performance; as a repository used by local teams and facility champions; and as a tool for team activation. The Hub enabled access to data that were previously inaccessible and unavailable and integrated that data with benchmark and scientific evidence to serve as a common data infrastructure. Led by champions, each implementation team used the Hub differently: local adoption of the staff and patient education materials; benchmarking facility performance against national rates and peer facilities; and positive reinforcement for QI plan development and monitoring. External facilitators used the Hub to help teams leverage data to target areas of improvement and disseminate local adaptations to promote resource sharing across teams. Conclusions As a dynamic platform for A&F operating within learning health systems, hubs represent a promising strategy to support local implementation of QI programs by newly formed, multidisciplinary teams.
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Ghafarzadeh, Habib, and Asgarali Bouyer. "An Efficient Hybrid Clustering Method Using an Artificial Bee Colony Algorithm and Mantegna Lévy Distribution." International Journal on Artificial Intelligence Tools 25, no. 02 (April 2016): 1550034. http://dx.doi.org/10.1142/s0218213015500347.
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Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Lévy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Lévy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation.
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Balasubramanian, Kishore, NP Ananthamoorthy, and K. Ramya. "Prediction of neuro-degenerative disorders using sunflower optimisation algorithm and Kernel extreme learning machine: A case-study with Parkinson’s and Alzheimer’s disease." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 3 (December 20, 2021): 438–53. http://dx.doi.org/10.1177/09544119211060989.
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Parkinson’s and Alzheimer’s Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer’s (AD) and Parkinson’s (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.
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Rowe, B., B. Bohlouli, and C. Villa-Roel. "MP08: Using administrative data to explore emergency department management of patients presenting with acute atrial fibrillation/flutter: Is Shock-First a more effective strategy than Drug-Shock?" CJEM 22, S1 (May 2020): S45. http://dx.doi.org/10.1017/cem.2020.156.
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Introduction: Atrial fibrillation and flutter (AFF) are the most common arrhythmias managed in the emergency department (ED). Equipoise in cardioversion strategies for patients with recent onset AFF contributes to observed practice variation. Using administrative data, the objective of this study was to explore the pattern of practice and the comparative effectiveness (outcomes and costs) between Shock-First and Drug-Shock approaches in AFF. Methods: Adult patients >17 years of age with AFF from one academic Canadian hospital ED were eligible. Using administrative data linkage among the National Ambulatory Care Record System, provincial practitioner claims data repository and a local hospital pharmacy database, patients who received treatment with procainamide and/or electrical cardioversion for AFF were identified. Outcomes including disposition, length of stay, revisit within 72 hours and 30-days, and ED costs were analyzed over a seven-year period. Categorical variables are reported as percentages. Continuous variables are reported as median and interquartile range (IQR). Univariate and multivariate logistic regression analyses were completed and reported as odds ratios (OR) and 95% confidence intervals (CI). Results: Overall, 5,372 patients were identified with AFF; the median age was 70 years and 55% were male. The majority of patients had chronic or secondary AFF; however, in 1687 (31%) cardioversion options were employed for presumed were recent onset AFF. A Shock-First strategy was most common (1379 {82%}); 308 (18%) received a Drug-Shock approach. Discharge time was 33 minutes (95% CI: 4–63) longer in the Drug-Shock approach compared to the Shock-First approach. Hospital admissions were higher (OR = 2.33; 95% CI: 1.68, 3.24) and revisits within 30-days were lower (OR = 0.74; 95% CI: 0.54, 0.95) in the Drug-Shock group. The Shock-First strategy demonstrated marginally higher costs (median = $106 CND; 95% CI: $68.89, $144.40) in adjusted analyses. Conclusion: In patients with acute AFF, when cardioversion was attempted, a Shock-First strategy was employed 80% of the time and resulted in shorter ED length of stay and lower hospitalization; however, higher costs and ED revisits within 30-days were observed. Many factors, including physician and/or patient preferences, influence ED decision-making in patients with AFF and understanding the factors influencing these decisions requires further attention.
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Wu, Xindong, Xingquan Zhu, and Minghui Wu. "The Evolution of Search: Three Computing Paradigms." ACM Transactions on Management Information Systems 13, no. 2 (June 30, 2022): 1–20. http://dx.doi.org/10.1145/3495214.
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Search is probably the most common activity that humans conduct all the time. A search target can be a concrete item (with a yes or no answer and location information), an abstract concept (such as the most important information on the Web about Xindong Wu), or a plan/path for a specific target with an objective function (like flight scheduling with a minimal travel time), among others. In this article, we propose a Universal Connection Theorem (UCT) to suggest that all physical objects/items in the universe are connected through explicit or implicit relationships. Search is to explore the relationships, using different computing methods, to retrieve relevant objects. Under the UCT theorem, we summarize mainstream search approaches into two categories from the user perspective, deterministic search vs. abstract search, and further distinguish them into three computing paradigms: planning based search, data driven search, and knowledge enhanced search. The planning based paradigm explores search as a planning process in a large search space, by graph traversing with heuristic principles to locate optimal solutions. The data driven paradigm seeks to find objects matching the user's query from a large data repository. Indexing, hashing, information retrieval, and recommendations are typical strategies to tackle the data volumes and select the best answers for users’ queries. The knowledge enhanced search does not aim to find matching objects, but to discover and then meet user's search requirements through knowledge mining. The evolution of these three search paradigms, from planning to data engineering and knowledge engineering, provides increasing levels of challenges and opportunities. This article elaborates the respective principles of these paradigms.
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Heiyanthuduwage, Thilini Madushika, Samanmali P. Sumanasena, Gopi Kitnasamy, Hayley Smithers Sheedy, Gulam Khandaker, Romaniya Fernando, Saraji Wijesekara, et al. "Protocol for the Sri Lankan Cerebral Palsy Register pilot study." BMJ Open 10, no. 8 (August 2020): e039353. http://dx.doi.org/10.1136/bmjopen-2020-039353.
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IntroductionCerebral palsy (CP) describes a heterogeneous group of motor disorders resulting from disturbance in the developing brain. CP occurs in approximately 2.1 per 1000 live births in high-income countries, but in low- and middle-income countries (LMICs) the prevalence and severity of CP may be greater and aetiological risk factors different. In Sri Lanka, a LMIC, there have been no epidemiological studies of CP to date. Systematically collected data are required to identify opportunities for primary and secondary prevention, to plan and establish services to support children and adults with CP and their families and to act as a sampling frame for new research. Here we describe a pilot study protocol for a CP register in Sri Lanka.Methods and analysisThe aim of this study is to establish a CP register in Sri Lanka. We will use different surveillance methodologies in two provinces of Sri Lanka: hospital and community surveillance in the Western Province and community surveillance in the Eastern Province. A common record form will collect demographic, clinical and service data for children with CP <18 years living in these two provinces. Data will be transferred to a secure online data repository and used to describe the epidemiology of CP in these regions. We will describe the strengths and challenges of the surveillance mechanisms and estimate the resources required for ongoing hospital and community based surveillance in the Western and Eastern provinces and to include additional provinces across the country.Ethics and disseminationThis study has ethical clearance from The University of Kelaniya, National Health Research Council, the Institutional Ethics Review Committee of the Lady Ridgeway Hospital, Colombo South Teaching Hospital and the Director of the North Colombo Teaching Hospital. Results from this research will be disseminated through local and international conferences and through publications in peer-reviewed journals.
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Vecchioni, Luca, Francesco Paolo Faraone, Fabio Stoch, Marco Arculeo, and Federico Marrone. "Diversity and Distribution of the Inland Water Decapods of Sicily (Crustacea, Malacostraca)." Diversity 14, no. 4 (March 27, 2022): 246. http://dx.doi.org/10.3390/d14040246.
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The current knowledge of Sicilian inland water decapod malacostracans is scarce and an updated synopsis on species distribution is lacking. Therefore, we reviewed the checklist and recent distribution of Sicilian inland water decapods based on published and unpublished records and novel observations with the aim of providing an exhaustive repository, also to be used as a sound baseline for future surveys. Overall, five native decapod species occur in the study area, i.e., the atyid shrimp Atyaephyra desmarestii, the palaemonid shrimps Palaemon adspersus, P. antennarius, and P. elegans, and the freshwater crab Potamon fluviatile, and their current local distributions are described. In addition, three alien species were recorded: the common yabby Cherax destructor and the red swamp crayfish Procambarus clarkii, strictly linked to inland waters, and the Atlantic blue crab Callinectes sapidus, a mainly marine species that can also colonise the lower stretches of rivers and coastal brackish waters. The collected data suggest the existence of a partial segregation of native versus non-native species, with the latter currently confined to coastal water bodies and the lower stretches of rivers. Moreover, the exclusively freshwater caridean A. desmarestii and P. antennarius show a parapatric distribution in the study area, which may suggest the existence of mutual exclusion phenomena. The results obtained raise some concerns about the effects of alien species on the native biota, and dedicated monitoring and management strategies should be implemented in order to better understand and mitigate their impact.
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Reguzzoni, Mirko, Daniela Carrion, Carlo Iapige De Gaetani, Alberta Albertella, Lorenzo Rossi, Giovanna Sona, Khulan Batsukh, et al. "Open access to regional geoid models: the International Service for the Geoid." Earth System Science Data 13, no. 4 (April 21, 2021): 1653–66. http://dx.doi.org/10.5194/essd-13-1653-2021.
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Abstract. The International Service for the Geoid (ISG, https://www.isgeoid.polimi.it/, last access: 31 March 2021) provides free access to a dedicated and comprehensive repository of geoid models through its website. In the archive, both the latest releases of the most important and well-known geoid models, as well as less recent or less known ones, are freely available, giving to the users a wide range of possible applications to perform analyses on the evolution of the geoid computation research field. The ISG is an official service of the International Association of Geodesy (IAG), under the umbrella of the International Gravity Field Service (IGFS). Its main tasks are collecting, analysing, and redistributing local, regional, and continental geoid models and providing technical support to people involved in geoid-related topics for both educational and research purposes. In the framework of its activities, the ISG performs research taking advantage of its archive and organizes seminars and specific training courses on geoid determination, supporting students and researchers in geodesy as well as distributing training material on the use of the most common algorithms for geoid estimation. This paper aims at describing the data and services, including the newly implemented DOI Service for geoid models (https://dataservices.gfz-potsdam.de/portal/?fq=subject:isg, last access: 31 March 2021), and showing the added value of the ISG archive of geoid models for the scientific community and technicians, like engineers and surveyors (https://www.isgeoid.polimi.it/Geoid/reg_list.html, last access: 31 March 2021).
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Brümmer, Christian, Jeremy J. Rüffer, Jean-Pierre Delorme, Pascal Wintjen, Frederik Schrader, Burkhard Beudert, Martijn Schaap, and Christof Ammann. "Reactive nitrogen fluxes over peatland and forest ecosystems using micrometeorological measurement techniques." Earth System Science Data 14, no. 2 (February 18, 2022): 743–61. http://dx.doi.org/10.5194/essd-14-743-2022.
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Abstract. Interactions of reactive nitrogen (Nr) compounds between the atmosphere and the earth's surface play a key role in atmospheric chemistry and in understanding nutrient cycling of terrestrial ecosystems. While continuous observations of inert greenhouse gases through micrometeorological flux measurements have become a common procedure, information about temporal dynamics and longer-term budgets of Nr compounds is still extremely limited. Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere–atmosphere exchange of selected Nr compounds over different land surfaces. The aim of the campaigns was to test and establish novel measurement techniques in eddy-covariance setups for continuous determination of surface fluxes of ammonia (NH3) and total reactive nitrogen (ΣNr) using two different analytical devices. While high-frequency measurements of NH3 were conducted with a quantum cascade laser (QCL) absorption spectrometer, a custom-built converter called Total Reactive Atmospheric Nitrogen Converter (TRANC) connected and operated upstream of a chemiluminescence detector (CLD) was used for the measurement of ΣNr. As high-resolution data of Nr surface–atmosphere exchange are still scarce but highly desired for testing and validating local inferential and larger-scale models, we provide access to campaign data including concentrations, fluxes, and ancillary measurements of meteorological parameters. Campaigns (n=4) were carried out in natural (forest) and semi-natural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface–atmosphere exchange in different ecosystems, thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen. The dataset has been placed in the Zenodo repository (https://doi.org/10.5281/zenodo.4513854; Brümmer et al., 2022) and contains individual data files for each campaign.
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Keränen, Niina, Timo Tuovinen, Jari Haverinen, Ronja Ruotanen, and Jarmo Reponen. "Regional health information exchange outside of the centralized national services for public health care in Finland: A national survey." Finnish Journal of eHealth and eWelfare 14, no. 1 (April 14, 2022): 31–42. http://dx.doi.org/10.23996/fjhw.111775.
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Health information exchange (HIE) is the mobilization of health care information electronically across organizations within a region, community, or hospital system. Nordic countries have been developing their health portals including national HIE services systematically. In Finland HIE begun with various regional health information exchange (RHIE) pilots in since 1998. The Kanta patient data repository component in the national HIE has been adopted in routine use since 2012. The current role of non-Kanta RHIE in relation to Kanta services is somewhat unclear. Our research questions are following: 1) Has the availability of RHIE services changed during 2017-2020? 2) What functional types of RHIE are there in Finland in 2020? 3) From the point of view of healthcare provider organizations, at what level is the availability to combine regional information seamlessly into the same view of local patient record systems? Data used in this study were collected using web-based questionnaires in 2017 and 2020 as part of the surveys for monitoring and assessment of social welfare and health care information system services in Finland. This study reported in this article covers all 21 public hospital districts and nearly all public primary health care centers. The quantitative data provided by the organizations were analyzed using SPSS software (version 25). The availability of a particular service or function was calculated as a percentage of all respondents in each sector. The results of this study show that the overall availability of RHIE services has not markedly changed 2017-2020. Functional types of RHIE meaning the role, use and types of RHIE in hospital districts in Finland varies greatly in 2020. We recognized three different types of non-Kanta RHIE in the non-combination organizations (one-way, symmetrical, full symmetrical). Seamless integration of at least some Kanta data into the same view as the main patient health record system data was more common than seamless integration of at least some non-Kanta regional data.
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Jonsdottir, Thora, Johann Thorsson, Ebba Thora Hvannberg, Jan Eric Litton, and Helgi Sigurdsson. "The Nordic Common Data Element repository for describing cancer data." International Journal of Metadata, Semantics and Ontologies 4, no. 4 (2009): 232. http://dx.doi.org/10.1504/ijmso.2009.029227.
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Olariu, Elena, Marian Sorin Paveliu, Eugen Baican, Yemi Oluboyede, Luke Vale, and Ileana Gabriela Niculescu-Aron. "Measuring health-related quality of life in the general population and Roma communities in Romania: study protocol for two cross-sectional studies." BMJ Open 9, no. 8 (August 2019): e029067. http://dx.doi.org/10.1136/bmjopen-2019-029067.
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IntroductionThe importance of health-related quality of life (HRQoL) is increasing and many healthcare authorities recommend the use of measures that account for both mortality and morbidity. This study will determine, for the first time in Romania, value sets for EuroQoL-five-dimensions-3-level (EQ-5D-3L) and EQ-5D-5L questionnaires and their population norms (study 1). It will also compare the HRQoL (measured with EQ-5D-5L) of Roma communities in Romania with that of the general population (study 2).Methods and analysisCross-sectional studies of face-to-face interviews conducted in representative samples of the Romanian general population and Romanian Roma communities. 1614 non-institutionalised adults older than 18 years will be interviewed using a computer-assisted interview for study 1. Participants will complete EQ-5D-3L and 5L, 13 composite time trade-off tasks (cTTO), 7 discrete choice experiment questions (DCE) and sociodemographic questions. For study 2, 606 non-institutionalised self-identified Roma people older than 18 years will be interviewed using a pencil-and-paper interview. Participants will complete EQ-5D-5L and the same sociodemographic questions as for study 1. The 3L value set will be estimated using econometric models and the cTTO data. cTTO and DCE data will be used for the 5L value set. Population norms will be reported by age and gender. The ORs for reporting different levels of problems and the most common health states in the population will be estimated. For study 2, t-tests and analysis of variance will be used to explore differences between groups in HRQoL and for each EQ-5D.Ethics and disseminationEthics approval was given by the National Bioethics Committee of Medicines and Medical Devices Romania and Newcastle University’s Research Ethics Committee. Results will be published in peer-reviewed journals, presented at scientific conferences and on the project’s website. The EQ-5D-5L anonymised datasets will be deposited in a centralised repository. Two public workshops with local authorities, physicians and patients’ associations will be held.
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Ainuson-Quampah, J., NN Amuna, M. Holdsworth, and R. Aryeetey. "A review of food-based dietary guidelines in Africa: Opportunities to enhance the healthiness and environmental sustainability of population diets." African Journal of Food, Agriculture, Nutrition and Development 22, no. 2 (April 4, 2022): 19471–95. http://dx.doi.org/10.18697/ajfand.107.21790.
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Food-based dietary guidelines (FBDGs) are important tools for promoting healthy eating and nutrition education at the population level. Currently, more than 100 countries worldwide have developed FBDGs with majority of existing FBDGs in high-income countries. However, there are a few countries in Africa which have developed FBDGs. This review describes and compares the characteristics of existing FBDGs in Africa. Data were extracted from all existing FBDGs from African countries which have been archived in the Food and Agriculture Organization’s (FAO) online repository of FBDGs. In addition, supporting documentation from other sources linked to the identified FBDGs were also reviewed. Extracted data were coded and synthesized to describe the purpose of the FBDGs, the process for developing the FBDGs, and how healthy diets were expressed in the FBDGs. In addition, the FBDGs were examined for content on considerations for planetary health, and non-dietary recommendations. A checklist was used to extract the evidence in the identified documents. Of the 47 African countries in the WHO Africa region, only eight FBDGs were identified from seven countries. South Africa had two FBDGs (including a FBDG for young children). Multi-disciplinary technical working groups were convened in developing majority of the FBDGs. The working groups utilised scientific evidence on diet-related diseases and nutrient requirements as a basis for recommendations. All the FBDGs were intended as a tool for promoting healthy diets among the lay population. The FBDGs that were reviewed included between six and sixteen dietary messages. Diet diversification was promoted as the most common recommendation across African country FBDGs. The recommendations often promoted consumption of four to six food groups communicated using text as well as images (food guide). Local availability and cultural acceptability were important values promoted as part of an optimal diet in some of the countries. However, none of the recommendations addressed environmental sustainability. Apart from South Africa, none of the FBDGs had been evaluated or revised. Across Africa, there is a need for increased focus on developing new FBDGs or revising existing ones as a tool for meeting the dietary information needs of populations at risk of malnutrition in all its forms. Key words: Food-based, Dietary, Guidelines, diversity, Africa, Health, Non-communicable, Sustainability
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Baum, B., J. Christoph, I. Engel, M. Löbe, S. Mate, S. Stäubert, J. Drepper, et al. "Integrated Data Repository Toolkit (IDRT)." Methods of Information in Medicine 55, no. 02 (2016): 125–35. http://dx.doi.org/10.3414/me15-01-0082.
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SummaryBackground: In recent years, research data warehouses moved increasingly into the focus of interest of medical research. Nevertheless, there are only a few center-independent infrastructure solutions available. They aim to provide a consolidated view on medical data from various sources such as clinical trials, electronic health records, epidemiological registries or longitudinal cohorts. The i2b2 framework is a well-established solution for such repositories, but it lacks support for importing and integrating clinical data and metadata.Objectives: The goal of this project was to develop a platform for easy integration and administration of data from heterogeneous sources, to provide capabilities for linking them to medical terminologies and to allow for transforming and mapping of data streams for user-specific views.Methods: A suite of three tools has been developed: the i2b2 Wizard for simplifying administration of i2b2, the IDRT Import and Mapping Tool for loading clinical data from various formats like CSV, SQL, CDISC ODM or biobanks and the IDRT i2b2 Web Client Plugin for advanced export options. The Import and Mapping Tool also includes an ontology editor for rearranging and mapping patient data and structures as well as annotating clinical data with medical terminologies, primarily those used in Germany (ICD-10-GM, OPS, ICD-O, etc.).Results: With the three tools functional, new i2b2-based research projects can be created, populated and customized to researcher’s needs in a few hours. Amalgamating data and metadata from different databases can be managed easily. With regards to data privacy a pseudonymization service can be plugged in. Using common ontologies and reference terminologies rather than project-specific ones leads to a consistent understanding of the data semantics.Conclusions: i2b2’s promise is to enable clinical researchers to devise and test new hypothesis even without a deep knowledge in statistical programing. The approach pre -sented here has been tested in a number of scenarios with millions of observations and tens of thousands of patients. Initially mostly observant, trained researchers were able to construct new analyses on their own. Early feedback indicates that timely and extensive access to their “own” data is appreciated most, but it is also lowering the barrier for other tasks, for instance checking data quality and completeness (missing data, wrong coding).
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Ferrell, Paul Brent, Caroline R. Maier, Mikael Roussel, Michael R. Savona, and Jonathan Michael Irish. "Expression of S100A9 in Bone Marrow Cells Differentiates Refractory Cytopenia with Multilineage Dysplasia (RCMD) from Refractory Anemia with Excess Blasts (RAEB) and Acute Myeloid Leukemia (AML)." Blood 128, no. 22 (December 2, 2016): 4303. http://dx.doi.org/10.1182/blood.v128.22.4303.4303.
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Abstract Introduction: Myelodysplastic syndrome (MDS) is a heterogeneous group of bone marrow disorders with a yearly incidence of approximately 13,000 in the United States. It has been observed that both genetic mutations within stem and progenitor cells and a disordered immune microenvironment are present early in MDS. Abnormal levels of inflammatory cytokines as well increased numbers of suppressive cell types, such as regulatory T cells and myeloid derived suppressor cells (MDSC) have been noted in MDS bone marrow. MDSC are recently discovered subset of myeloid cells with specific immune regulatory functions, such as T cells suppression, seen in pathological conditions, such as cancer. Recent data suggest MDSC may play a critical role in MDS pathogenesis, and that S100A9, a danger-associated molecular pattern (DAMP) produced by some myeloid cells, including neutrophils, monocytes and MDSC, is a key signal for bone marrow immune dysregulation. Here, we report a systems immunology approach to cell type discovery within MDS bone marrow using high dimensional mass cytometry. Methods: Bone marrow aspirate samples with informed consent from MDS (n=19) and AML (n=4) patients were collected and cryopreserved following red blood cell lysis for storage by the Vanderbilt Hematology Tissue Repository, a tissue repository approved by the local Institutional Review Board (IRB). Samples were acquired for the study and stained with a 35-marker panel of metal tagged mass cytometry antibodies and analyzed with a mass cytometer (CyTOF). Cellular populations were then characterized using biaxial gating as well as viSNE, SPADE and hierarchical clustering as has been previously reported (Diggins et al. Methods 2015, Ferrell et al. PLoS One, 2016). Results: Unsupervised viSNE analysis of 35-markers per cell revealed distinct cellular subsets within each sample. Interestingly, one of the strongest marker signals was expression of S100A9, which was seen in multiple cells types including phenotypic MDSC. Further analysis revealed that as a percentage of bone marrow cells, S100A9 expression was significantly more common in RCMD vs. RAEB and AML (30.0% (n=10) vs. 10.9% (n=9) and 2.4% (n=4), respectively, p<0.05 for each comparison) (Figure 1A). Additionally, three paired RCMD/AML samples were available for analysis. Within these patients, the percentage of S100A9+ cells dropped from a mean of 41.7% in RCMD to a mean of 1.84% in AML bone marrow (Figure 1B&C). Conclusion: S100A9 is both a distinguishing feature of RCMD and of disease progression within MDS. Because of its important role inflammation and cellular recruitment, S100A9 may correlate with bone marrow cellular inflammation and could represent a viable target in treatment of the disordered immune microenvironment present in MDS, especially RCMD. Disclosures Savona: Celgene: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Membership on an entity's Board of Directors or advisory committees; Amgen Inc.: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Takeda: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees. Irish:Incyte: Research Funding; Janssen: Research Funding; Cytobank, Inc.: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.
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Sharma, Seemu, and Seema Bawa. "CBDR: An efficient storage repository for cultural big data." Digital Scholarship in the Humanities 35, no. 4 (November 15, 2019): 893–903. http://dx.doi.org/10.1093/llc/fqz083.
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Abstract Cultural data and information on the web are continuously increasing, evolving, and reshaping in the form of big data due to globalization, digitization, and its vast exploration, with common people realizing the importance of ancient values. Therefore, before it becomes unwieldy and too complex to manage, its integration in the form of big data repositories is essential. This article analyzes the complexity of the growing cultural data and presents a Cultural Big Data Repository as an efficient way to store and retrieve cultural big data. The repository is highly scalable and provides integrated high-performance methods for big data analytics in cultural heritage. Experimental results demonstrate that the proposed repository outperforms in terms of space as well as storage and retrieval time of Cultural Big Data.
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Dos Santos, Marcio Carneiro. "Métodos digitais e a memória acessada por APIs: desenvolvimento de ferramenta para extração de dados de portais jornalísticos a partir da WayBack Machine." Revista Observatório 1, no. 2 (December 8, 2015): 23. http://dx.doi.org/10.20873/uft.2447-4266.2015v1n2p23.
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Explora-se a possibilidade de automação da coleta de dados em sites, a partir da aplicação de código construído em linguagem de programação Python, utilizando a sintaxe específica do HTML (HiperText Markup Language) para localizar e extrair elementos de interesse como links, texto e imagens. A coleta automatizada de dados, também conhecida como raspagem (scraping) é um recurso cada vez mais comum no jornalismo. A partir do acesso ao repositório digital do site www.web.archive.org, também conhecido como WayBackMachine, desenvolvemos a prova de conceito de um algoritmo capaz de recuperar, listar e oferecer ferramentas básicas de análise sobre dados coletados a partir das diversas versões de portais jornalísticos ao longo do tempo. PALAVRAS-CHAVE: Raspagem de dados. Python Jornalismo Digital. HTML. Memória. ABSTRACTWe explore the possibility of automation of data collection from web pages, using the application of customized code built in Python programming language, with specific HTML syntax (Hypertext Markup Language) to locate and extract elements of interest as links, text and images. The automated data collection, also known as scraping is an increasingly common feature in journalism. From the access to the digital repository site www.web.archive.org, also known as WayBackMachine, we develop a proof of concept of an algorithm able to recover, list and offer basic tools of analysis of data collected from the various versions of newspaper portals in time series.KEYWORDS: Scraping. Python. Digital Journalism. HTML. Memory. RESUMENSe explora la posibilidad de automatización de los sitios de recolección de datos, desde el código de aplicación construida en lenguaje de programación Python, utilizando la sintaxis específica de HTML (Hypertext Markup Language) para localizar y extraer elementos de interés, tales como enlaces, texto e imágenes. La colección de datos automatizada, también conocido como el raspado es una característica cada vez más común en el periodismo. Desde el acceso a la www.web.archive.org, sitio de repositorio digital, también conocida como WayBackMachine, desarrollamos una prueba de concepto de un algoritmo para recuperar, listar y ofrecer herramientas básicas de análisis de los datos recogidos de las diferentes versiones de portales de periódicos en el tiempo. PALABRAS CLAVE: Raspar datos. Python. Periodismo digital. HTML. Memoria. ReferênciasBIRD, Steven; LOPER, Edward; KLEIN, Ewan. Natural Language Processing with Python: analyzing text with the Natural Language Toolkit. New York: O'Reilly Media Inc., 2009.BONACICH, Phillip; LU, Phillip. Introduction to mathematical sociology. New Jersey: Princeton University Press, 2012.BRADSHAW, Paul. Scraping for Journalists. Leanpub, 2014, [E-book].GLEICK, James. A Informação. Uma história, uma teoria, uma enxurrada. São Paulo, Companhia das Letras, 2013.MANOVICH, Lev. The Language of New Media. Cambrige: Mit Press, 2001.MORETTI, Franco. Graphs, maps, trees. Abstract models for literary history. New York, Verso, 2007.ROGERS, Richard. Digital Methods. Cambridge: Mit Press, 2013. E-book.SANTOS, Márcio. Conversando com uma API: um estudo exploratório sobre TV social a partir da relação entre o twitter e a programação da televisão. Revista Geminis, ano 4 n. 1, p. 89-107, São Carlos. 2013. Disponível em: . Acesso em: 20 abr. 2013.SANTOS, Márcio. Textos gerados por software. Surge um novo gênero jornalístico. Anais XXXVII Congresso Brasileiro de Ciências da Comunicação. Foz do Iguaçu, 2014. Disponível em: . Acesso em 26 jan. 2014. Disponível em:Url: http://opendepot.org/2682/ Abrir em (para melhor visualização em dispositivos móveis - Formato Flipbooks):Issuu / Calameo
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Samra, Halima, Alice Li, and Ben Soh. "GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data." Healthcare 8, no. 3 (August 6, 2020): 257. http://dx.doi.org/10.3390/healthcare8030257.
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There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.
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Hornik, Christoph, Andrew Atz, Catherine Bendel, Francis Chan, Kevin Downes, Robert Grundmeier, Ben Fogel, et al. "Creation of a Multicenter Pediatric Inpatient Data Repository Derived from Electronic Health Records." Applied Clinical Informatics 10, no. 02 (March 2019): 307–15. http://dx.doi.org/10.1055/s-0039-1688477.
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Background Integration of electronic health records (EHRs) data across sites and access to that data remain limited. Objective We developed an EHR-based pediatric inpatient repository using nine U.S. centers from the National Institute of Child Health and Human Development Pediatric Trials Network. Methods A data model encompassing 147 mandatory and 99 optional elements was developed to provide an EHR data extract of all inpatient encounters from patients <17 years of age discharged between January 6, 2013 and June 30, 2017. Sites received instructions on extractions, transformation, testing, and transmission to the coordinating center. Results We generated 177 staging reports to process all nine sites' 147 mandatory and 99 optional data elements to the repository. Based on 520 prespecified criteria, all sites achieved 0% errors and <2% warnings. The repository includes 386,159 inpatient encounters from 264,709 children to support study design and conduct of future trials in children. Conclusion Our EHR-based data repository of pediatric inpatient encounters utilized a customized data model heavily influenced by the PCORnet format, site-based data mapping, a comprehensive set of data testing rules, and an iterative process of data submission. The common data model, site-based extraction, and technical expertise were key to our success. Data from this repository will be used in support of Pediatric Trials Network studies and the labeling of drugs and devices for children.
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Hahnel, Mark, and Dan Valen. "How to (Easily) Extend the FAIRness of Existing Repositories." Data Intelligence 2, no. 1-2 (January 2020): 192–98. http://dx.doi.org/10.1162/dint_a_00041.
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Data repository infrastructures for academics have appeared in waves since the dawn of Web technology. These waves are driven by changes in societal needs, archiving needs and the development of cloud computing resources. As such, the data repository landscape has many flavors when it comes to sustainability models, target audiences and feature sets. One thing that links all data repositories is a desire to make the content they host reusable, building on the core principles of cataloging content for economical and research speed efficiency. The FAIR principles are a common goal for all repository infrastructures to aim for. No matter what discipline or infrastructure, the goal of reusable content, for both humans and machines, is a common one. This is the first time that repositories can work toward a common goal that ultimately lends itself to interoperability. The idea that research can move further and faster as we un-silo these fantastic resources is an achievable one. This paper investigates the steps that existing repositories need to take in order to remain useful and relevant in a FAIR research world.
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Maier, Christian, Lorenz A. Kapsner, Sebastian Mate, Hans-Ulrich Prokosch, and Stefan Kraus. "Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model." Applied Clinical Informatics 12, no. 01 (January 2021): 057–64. http://dx.doi.org/10.1055/s-0040-1721481.
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Abstract Background The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.
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Moser, Thomas, Stefan Biffl, Wikan Danar Sunindyo, and Dietmar Winkler. "Integrating Production Automation Expert Knowledge Across Engineering Domains." International Journal of Distributed Systems and Technologies 2, no. 3 (July 2011): 88–103. http://dx.doi.org/10.4018/jdst.2011070106.
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The engineering of a complex production automation system involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional integration approaches to data integration using a common repository are limited as they require an agreement on a common data schema by all project stakeholders. This paper introduces the Engineering Knowledge Base (EKB), a semantic-web-based framework, which supports the efficient integration of information originating from different expert domains without a complete common data schema. The authors evaluate the proposed approach with data from real-world use cases from the production automation domain on data exchange between tools and model checking across tools. Major results are that the EKB framework supports stronger semantic mapping mechanisms than a common repository and is more efficient if data definitions evolve frequently.
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J H, Ma, He S J, and Liu C. "Common Interesting on Coordination of Global Change Research Data Repository and Academic Journals." Journal of Global Change Data & Discovery 2, no. 4 (2018): 473–76. http://dx.doi.org/10.3974/geodp.2018.04.19.
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Frank, Rebecca D., Kara Suzuka, Eric Johnson, and Elizabeth Yakel. "Tool Selection Among Qualitative Data Reusers." International Journal of Digital Curation 15, no. 1 (August 5, 2020): 15. http://dx.doi.org/10.2218/ijdc.v15i1.710.
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This paper explores the tension between the tools that data reusers in the field of education prefer to use when working with qualitative video data and the tools that repositories make available to data reusers. Findings from this mixed-methods study show that data reusers utilizing qualitative video data did not use repository-based tools. Rather, they valued common, widely available tools that were collaborative and easy to use.
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Arenson, Andrew D., Ludmila N. Bakhireva, Christina D. Chambers, Christina A. Deximo, Tatiana Foroud, Joseph L. Jacobson, Sandra W. Jacobson, et al. "Implementation of a shared data repository and common data dictionary for fetal alcohol spectrum disorders research." Alcohol 44, no. 7-8 (November 2010): 643–47. http://dx.doi.org/10.1016/j.alcohol.2009.08.007.
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Reimer, Andrew P., and Alex Milinovich. "Using UMLS for electronic health data standardization and database design." Journal of the American Medical Informatics Association 27, no. 10 (September 17, 2020): 1520–28. http://dx.doi.org/10.1093/jamia/ocaa176.
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Abstract Objective Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system. Methods The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching. Results 431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped. Discussion The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format. Conclusion The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.
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Сикулер, Денис Валерьевич. "RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES." Информационные и математические технологии в науке и управлении, no. 2(22) (June 25, 2021): 39–52. http://dx.doi.org/10.38028/esi.2021.22.2.004.
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В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.
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Mendelsohn, Emma, Noam Ross, Allison M. White, Karissa Whiting, Cale Basaraba, Brooke Watson Madubuonwu, Erica Johnson, et al. "A global repository of novel antimicrobial emergence events." F1000Research 9 (June 21, 2021): 1320. http://dx.doi.org/10.12688/f1000research.26870.2.
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Despite considerable global surveillance of antimicrobial resistance (AMR), data on the global emergence of new resistance genotypes in bacteria has not been systematically compiled. We conducted a study of English-language scientific literature (2006-2017) and ProMED-mail disease surveillance reports (1994-2017) to identify global events of novel AMR emergence (first clinical reports of unique drug-bacteria resistance combinations). We screened 24,966 abstracts and reports, ultimately identifying 1,757 novel AMR emergence events from 268 peer-reviewed studies and 26 disease surveillance reports (294 total). Events were reported in 66 countries, with most events in the United States (152), China (128), and India (127). The most common bacteria demonstrating new resistance were Klebsiella pneumoniae (344) and Escherichia coli (218). Resistance was most common against antibiotic drugs imipenem (89 events), ciprofloxacin (84) and ceftazidime (83). We provide an open-access database of emergence events with standardized fields for bacterial species, drugs, location, and date. We discuss the impact of reporting and surveillance bias on database coverage, and we suggest guidelines for data analysis. This database may be broadly useful for understanding rates and patterns of AMR evolution, identifying global drivers and correlates, and targeting surveillance and interventions.
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Williams, Dean N., V. Balaji, Luca Cinquini, Sébastien Denvil, Daniel Duffy, Ben Evans, Robert Ferraro, Rose Hansen, Michael Lautenschlager, and Claire Trenham. "A Global Repository for Planet-Sized Experiments and Observations." Bulletin of the American Meteorological Society 97, no. 5 (May 1, 2016): 803–16. http://dx.doi.org/10.1175/bams-d-15-00132.1.
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Abstract Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.
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Chen, Henry W., Jingcheng Du, Hsing-Yi Song, Xiangyu Liu, Guoqian Jiang, and Cui Tao. "Representation of Time-Relevant Common Data Elements in the Cancer Data Standards Repository: Statistical Evaluation of an Ontological Approach." JMIR Medical Informatics 6, no. 1 (February 22, 2018): e7. http://dx.doi.org/10.2196/medinform.8175.
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(Helen) Pan, Huaqin, and Carol Hamilton. "Letter to Editor: Consistent Reporting of Reusable PhenX CDEs in the NIH Common Data Elements Repository." Journal of Biomedical Informatics 120 (August 2021): 103845. http://dx.doi.org/10.1016/j.jbi.2021.103845.
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Kremer, Andreas, Reinhard Schneider, and Georg C. Terstappen. "A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration." Bioscience Reports 25, no. 1-2 (February 4, 2005): 95–106. http://dx.doi.org/10.1007/s10540-005-2850-4.
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The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.
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Sahu, Lalita, and Meeta Jha. "Academic Stress in relation to Personality, Locale and Gender." Journal of Ravishankar University (PART-A) 26, no. 1 (February 12, 2021): 25–34. http://dx.doi.org/10.52228/jrua.2020-26-1-4.
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School education is very important part in an individual’s life and is also a turning point in their academic life. Stress is a common factor among school students. Academic stress involves mental distress regarding anticipated academic challenges or failure or even the fear of the possibility of academic failure. A student’s life is subjected to different kinds of stressors such as the pressure of academics with a requirement of success, uncertain future and difficulties predict for integration into the system. In the present scenario academic stress is major factor that influence the students’ performance as well as their personality. The study investigated academic stress in relation to personality, locale and gender among higher secondary school students. A total of four hundred male and female students participated in this study from Baster district of Chhattisgarh. The sample was selected by using stratified random sampling technique. To assess the personality of the subjects, Eysenck’s Maudsley Personality Inventory (MPI) and Academic Stress Scale for Students was used. Obtained data were analyzed using descriptive statistics, t-test, and regression analysis. The results show significant difference between academic stress and gender, academic stress and locale, personality and locale of students. No gender difference was found between the personality of male and female students. Results also indicated that boys experienced more academic stress as compared to girls. Similarly, non-tribal students experienced higher level of academic stress as compared to tribal students. The result of regression analysis showed that personality emerged out as a significant predictor of students’ academic stress explaining about 30.5% variation.
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Zotov, Vladimir, Iman Ibrahim, Irina Petunina, and Yuliya Lazareva. "Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement." International Journal of Emerging Technologies in Learning (iJET) 16, no. 02 (January 26, 2021): 46. http://dx.doi.org/10.3991/ijet.v16i02.18745.
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The study describes an approach to e-learning based on the Moodle platform that is used to visualize participation in the learning community and is proposed to be used to inform students and teachers about their involvement in the social learn-ing environment. The experiment involved 5 teachers and 3 experts who deter-mined the most significant visualization indicators for the virtual learning envi-ronment dashboards. There were 42 students aged 21 to 23. The virtual learning environment is based on the Moodle and Blackboard platforms that are common-ly used in universities. SocialWall allowed participants to perform actions in the social environment that are visualized in graphs under the specified criteria. A Wiki repository plugin was also added in order to accumulate student knowledge in shared structured documents stored in a shared repository. The relational data-base management system MySQL allows creation of additional relations, data-base design and administration. The visualization activities described in the study are based on modified state transition networks to analyze and visualize the stu-dent learning path. Student trajectory networks show the interaction of individual learners or groups with the course structure and material.
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Poppleton, Erik, Aatmik Mallya, Swarup Dey, Joel Joseph, and Petr Šulc. "Nanobase.org: a repository for DNA and RNA nanostructures." Nucleic Acids Research 50, no. D1 (November 8, 2021): D246—D252. http://dx.doi.org/10.1093/nar/gkab1000.
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Abstract We introduce a new online database of nucleic acid nanostructures for the field of DNA and RNA nanotechnology. The database implements an upload interface, searching and database browsing. Each deposited nanostructures includes an image of the nanostructure, design file, an optional 3D view, and additional metadata such as experimental data, protocol or literature reference. The database accepts nanostructures in any preferred format used by the uploader for the nanostructure design. We further provide a set of conversion tools that encourage design file conversion into common formats (oxDNA and PDB) that can be used for setting up simulations, interactive editing or 3D visualization. The aim of the repository is to provide to the DNA/RNA nanotechnology community a resource for sharing their designs for further reuse in other systems and also to function as an archive of the designs that have been achieved in the field so far. Nanobase.org is available at https://nanobase.org/.
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Ali, Nzar A., and Zhyan M. Omer. "Improving accuracy of missing data imputation in data mining." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 66–73. http://dx.doi.org/10.24017/science.2017.3.30.
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In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always clean and includes noise, incomplete data, duplicate records, inconsistent data and missing values. Missing data is a common drawback in many real-world data sets. In this paper, we proposed an algorithm depending on improving (MIGEC) algorithm in the way of imputation for dealing missing values. We implement grey relational analysis (GRA) on attribute values instead of instance values, and the missing data were initially imputed by mean imputation and then estimated by our proposed algorithm (PA) used as a complete value for imputing next missing value.We compare our proposed algorithm with several other algorithms such as MMS, HDI, KNNMI, FCMOCS, CRI, CMI, NIIA and MIGEC under different missing mechanisms. Experimental results demonstrate that the proposed algorithm has less RMSE values than other algorithms under all missingness mechanisms.
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Makhni, Sonya, Bruno Barbarioli, Sanjay Krishnan, Jonathan Ozik, Kevin Lazenby, Marc Sleiman, Ankur Bhargava, Julian Solway, Ellen Cohen, and Samuel Volchenboum. "128 Creating a data repository of sociomic factors to further characterize clinical outcomes and disease progression in patients with asthma." Journal of Clinical and Translational Science 6, s1 (April 2022): 8. http://dx.doi.org/10.1017/cts.2022.42.
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OBJECTIVES/GOALS: To assemble publicly-available, proprietary, and geocoded datasets about social, environmental, behavioral, and psychological exposures experienced by children with asthma, to provide a technical overview of data aggregation, management, and integration processes utilized, and to build predictive models using sociome and clinical data. METHODS/STUDY POPULATION: Publicly-available data involving census information, crime, green space, building permits, vacant and abandoned buildings, traffic (City of Chicago data portal), pollution and weather (National Oceanic and Atmospheric Administration), and noise (Array of Things project) were assembled. We placed a local instance of the Pelias geocoder on the UChicago Center for Research Informatics HIPAA-compliant infrastructure. The UChicago Clinical Research Data Warehouse will be leveraged to obtain clinical information for children diagnosed with asthma at UChicago Medicine between 2007 and 2021. The address of each child will be subjected to geocoding, and this information will be aligned with imported sociome data. A model will be built to account for each sociome elements contribution to asthma outcomes. RESULTS/ANTICIPATED RESULTS: Here we are creating sustainable and scalable ways for collecting, standardizing, and sharing real-world sociome data, simultaneously linking those data back to patient information. With this work, we aim to demonstrate feasibility of a data-commons-as-a-service for clinical and sociome data and to provide technical specifications and descriptions of processes employed. Creating generalizable and scalable infrastructure to support research of social and environmental impacts on clinical outcomes is critical, and our work will provide a framework to be used in other disease states. Further, this infrastructure will facilitate the application of advanced analytical tools and visualization platforms to accelerate the study of diseases and lead to new insights into factors influencing outcomes. DISCUSSION/SIGNIFICANCE: Beyond focusing on and treating biological mechanisms of disease, advancing health also requires addressing adverse consequences of sociome factors on clinical outcomes. We describe an innovative process to comprehensively codify and quantify such information in a way suitable for large scale co-analysis with biological and clinical data.