Data Definition Research Articles

Researching on Data Culture and Its Application in Solving Organizational Problems

The volume of data that individuals & businesses create tends to increase rapidly, leading to the challenge of how to leverage them to create real value? That's why more and more businesses are engaged in pursuing Data Culture & orienting it to become the number one priority (according to Gartner's survey). However, the transition from instinct-driven decision-making to data-driven decision making is not an easy process. From culture to logistics, there are countless barriers that organizations must navigate in order to make optimal use of cultural data to their benefit. This Paper is based in the following Research Design. It first seeked the theoretical framework on Data-Driven Culture, Data-Driven Marketing and its importance in an Organization. Then, via the application of Data Analytics – An application that analyzes data to identify problems and solutions, the Paper then proposes this data as the backbone for all activities and decisions in the process of planning, implementing, and optimizing operations. Thirdly, it researches a sample case study of Common types of data in Performance Marketing and Marketing data analysis process. The study has shown very significant results. One of the primary outcomes of this research is to find out the definition of Data Driven Culture, Data-Driven Marketing and related theorectical framworks. In addition, the findings showed a Case Study of Performance Marketing which reveals the Data Driven Culture, Data-Driven Marketing and its applications for business decison making and for Enterprises problems solving. Keywords: Data-driven culture, data-driven marketing, performance marketing, data-driven decision making

Development and challenges of tobacco epidemic surveillance in China

Tobacco use is the leading preventable risk factor causing the global burden of disease and one of China's most significant public health issues. Continuous and dynamic tobacco monitoring can provide critical index data for developing tobacco control strategies and measures and evaluating the impact of tobacco control. The WHO has incorporated monitoring programs into the crucial content of tobacco control and put forward relevant compliance requirements in the Framework Convention on Tobacco Control (FCTC). The Chinese government has actively promoted tobacco control significantly since the entry into force of the FCTC in China and continuously strengthened tobacco monitoring. In 2021, China's tobacco monitoring was honored to have reached the highest level required by the WHO. This study introduces the sampling design of tobacco surveys, monitoring content, the definition of key indicators and data weighting based on a complex sampling design, analyzing the challenges facing in the current setting, and provides a reference for the understanding and utilization of the data, the comparison of the results, and the future development of tobacco monitoring. The surveys involved in this study will focus on the specialized epidemiological surveys of tobacco for adults and adolescents carried out nationwide.

Review of the Performance Metrics for Natural Language Systems for Clinical Trials Matching.

Natural Language Processing (NLP) has been adopted widely in clinical trial matching for its ability to process unstructured text that is often found in electronic health records. Despite the rise in the new tools that use NLP to match patients to eligible clinical trials, the comparison of these tools is difficult due to the lack of consistency in how these tools are evaluated. The ground truth or reference that the tools use to assess results varies, making it difficult to compare the robustness of the tools against each other. This paper alarms the lack of definition and consistency of ground truth data used to evaluate such tools and suggests two ways to define a gold standard for the ground truth in small and large-scale studies.

Tracing COVID-19 Infection Chains Within Healthcare Institutions - Another Brick in the Wall Against SARS-CoV-2.

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.

Exploring Boundedness for Concept‐Based Instruction of Aspect: Evidence From Learning the Spanish Preterite and Imperfect

AbstractThis study investigated to what extent and how novice second‐language (L2) Spanish learners use the cognitive linguistics‐informed concept of boundedness to support their development of aspect—namely, the Spanish preterite and imperfect. The concept was presented within the framework of concept‐based instruction (CBI) as part of a regular, semester‐long U.S. college Spanish course for true beginners (N = 16). The sociocultural approach of CBI has been found to be effective for other complex L2 phenomena with more advanced learners, but the usefulness of the concept of boundedness for novice learners was untested. Definition, comprehension, verbalization, and performance data were triangulated to measure instructional effectiveness. Results suggest that the concept‐based instructional approach using the concept of boundedness supported students’ understanding of aspect, evidenced by multiple measures of conceptual development, as well as their accuracy in using aspect to create personal narratives.

"The relevance of strategic planning for the development of physical education and physical activity of children and youth (UNESCO international initiatives")

The article by discusses regulatory, planning and project activities of influential international organizations interested in in systematic strong progress in the field of physical education, open access to such classes, promotion of healthy lifestyles, monitoring its quality in educational institutions, including UNESCO. The authors outlined the definition of real data on the distribution of time in physical education curricula and their content (goals and topics, activities, relevance of the introduction of certain types of education). in physical activity, quality); monitoring the quality of physical education in schools around the world; staffing and logistics; finding ways of partnership in order to implement UNESCO projects. The article also considers the general principles of the organization's policy on the accessibility of both the process of physical education and physical culture and sports facilities. Named interesting ideas of the authors of the "World Survey on School Physical Education" in 2013 on the introduction of alternative physical activities for those exempted from physical education on the basis of doctor's recommendations, promotion and development of physical culture and sports, in particular in the context of job creation and, consequently, economic growth. In addition, the selected experience introduces norms to increase the duration of both mandatory lessons (from 40 to 80 minutes per week) and additional extracurricular activities.

Modeling of non-proportional multiaxial fatigue under synchronous and asynchronous sinusoidal loading conditions

Multi-axial asynchronous loading presents a unique challenge in fatigue analysis of structural components. A comprehensive investigation is carried out into asynchronous multi-axial fatigue through the framework of path dependent maximum range cycle counting methods (PDMR). Through the investigation two key aspects of asynchronous loading: 1) Unclear cycle definition, and 2) Non-proportionality across different frequency ratios are addressed. The PDMR framework enables a uniform cycle definition and superior correlation of synchronous and asynchronous experimental data. Systematic analysis across a various asynchronous loading is carried out to provide insight into the damage they cause.

A rank subdivision of equivalent score for enhancing neuropsychological test norms

IntroductionNeuropsychological assessment of cognitive functioning is a crucial part of clinical care: diagnosis, treatment planning, treatment evaluation, research, and prediction of long-term outcomes. The Equivalent Score (ES) method is used to score numerous neuropsychological tests. The ES0 and the ES4 are defined respectively by the outer tolerance limit and the median. The intermediate ESs are commonly calculated using a z-score approach even when the distribution of neuropsychological data is typically non-parametric. To calculate more accurate ESs, we propose that the intermediate ESs need to be calculated based on a non-parametric rank subdivision of the distribution of the adjusted scores.Material and methodsWe make three simulations to explain the differences between the classical z-score approach, the rank-based approach, and the direct subdivision of the dependent variable.ResultsThe results show that the rank procedure permits dividing the region between ES0 and ES4 into three areas with the same density. The z-score procedure is quite similar to the direct subdivision of the dependent variable and different from the rank subdivision.ConclusionsBy subdividing intermediate ESs using the rank-subdivision, neuropsychological tests can be scored more accurately, also considering that the two essential points for diagnosis (ES = 0 and ES = 4) remain the same. Future normative data definition should consider the best procedure for scoring with ES.

Creation of data application to facilitate device-monitoring safety for infection prevention

Background: Efficient monitoring of devices to ensure timely removal is an ongoing challenge. There is a need for data visualization products that can aggregate disparate data streams to support device reviews, increase consistency across changes in caregiver teams, and synergize with people and operational processes within and across regional acute-care facilities. Methods: A data display application was developed to provide data from nearly any source in a consistent visual representation that could be used in real time. The infection prevention (IP) overlay combined data related to urinary catheters, central vascular catheters, and femoral vascular catheters from the electronic health record system. Clinical and data experts collaborated to develop data definitions, inclusion criteria, and report components. The application display indicated the current catheter or device status of each patient facility-wide, organized by service unit (Fig. 1). Additional patient information could be accessed from within the application, and a comment feature allowed caregivers to communicate directly through the tool (Fig. 2). Results: Pilot implementation began February 2021, and the NATE IP application was live for all users (unit and facility leaders, providers, infection preventionists, etc) as of July 2021. The tool is currently available for use at 171 acute-care hospitals within the HCA healthcare system, and it accommodated 3 different electronic medical record systems. Usage peaked in August 2021, with an average of 1,700 views per day. Daily utilization maximum ranges from 1,100 to 1,500 views per day, with an average of ~1,300 views per day. The tool is used during daily patient safety rounds, including weekends and holidays. User feedback was overwhelmingly positive, with users reporting an increase in communication, streamlined documentation, improved tracking of reasons to retain, and increased accountability for daily updates. During the proof-of-concept implementation, zero bugs were identified and several feature enhancements were implemented, including addition of port status and device-day reporting counts. Planned enhancements include mupirocin and chlorhexidine bathing use, isolation precaution use, and blood cultures ordered >3 days after admission. Conclusions: The NATE IP tool brings together data related devices into a single view for use by direct caregivers and all levels of leadership. Development of this or similar tools to consolidate various data streams into a central tool facilitates improved communication and consistency between caregiver teams. It also drives operational efficiencies and improves safety. Expansion to incorporate notifications related to potential issue will expand the proactive utility of this tool.Funding: NoneDisclosures: None

Dynamic Target Tracking Method Based on Medical Imaging

The cross fusion of rehabilitation medicine and computer graphics is becoming a research hotspot. Due to the problems of low definition and unobvious features of the initial video data of medical images, the initial data is filtered and enhanced by adding image preprocessing, including image rotation and contrast enhancement, in order to improve the performance of the tracking algorithm. For the moving barium meal, the discrete point tracking and improved inter frame difference method are proposed; for the position calibration of tissues and organs, the Kernel Correlation Filter (KCF) and Discriminative Scale Space Tracker (DSST) correlation filtering method and the corresponding multi-target tracking method are proposed, and the experimental results show that the tracking effect is better. The two algorithms modify each other to further improve the accuracy of calibration and tracking barium meal flow and soft tissue organ motion, and optimize the whole swallowing process of moving target tracking model.

Stylistic text characteristics of Mari folk fairy tales

Introduction. The relevance of the article is determined by the need to identify and describe the stylistic characteristics of the texts of folklore genres, as well as revealing the composition of the identified expressive means and figurative techniques of the Mari language. With a modern interest to the spiritual heritage of nations, the development of philological sciences, an analysis of the stylistic characteristics of the texts of Mari fairy tales can provide a more complete picture of the language and style of Mari folklore, which is an integral part of the system of functional styles of the Mari language. The article presents new data on the systemic nature of stylistic devices in the texts of folk tales. Materials and Methods. The empirical material is presented by 133 Mari folk fairy tales borrowed from various sources and considered from the stylistics within the framework of the theory of systems using a combination of general scientific (analysis and synthesis) and specific stylistic (semantic-stylistic, stylistic interpretation of the text) methods. Results and Discussion. The identified and described set of expressive and figurative means testifies to the wide variety of the components of folklore functional style, which is part of the system of styles of the Mari language. The algorithm for studying expressive means and figurative techniques in the texts of fairy tales presented in the article makes it possible to use the theoretical basis and methodological base in the study of texts of other folklore genres that belong not only to the Mari linguistic culture. Conclusion. The relevance and significance of the research is determined by the tasks set in it resulting in definition of new data on the systemic nature and components of the style of folk fairy tales. The main part of the system of stylistic devices is made up of expressive techniques of the phonological and syntactic levels of texts, supplemented by techniques of the morphological and lexical levels.

Correction to: Revisiting the definition of health data in the age of digitalized health care

Journal Article Correction to: Revisiting the definition of health data in the age of digitalized health care Get access International Data Privacy Law, Volume 12, Issue 2, May 2022, Page 162, https://doi.org/10.1093/idpl/ipac010 Published: 20 April 2022

Data standards for heart failure: the European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart).

Standardized data definitions are essential for assessing the quality of care and patient outcomes in observational studies and randomized controlled trials. The European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart) project of the European Society of Cardiology (ESC) aims to create contemporary pan-European data standards for cardiovascular diseases, including heart failure (HF). We followed the EuroHeart methodology for cardiovascular data standard development. A Working Group including experts in HF registries, representatives from the Heart Failure Association of the ESC, and the EuroHeart was formed. Using Embase and Medline (2016–21), we conducted a systematic review of the literature on data standards, registries, and trials to identify variables pertinent to HF. A modified Delphi method was used to reach a consensus on the final set of variables. For each variable, the Working Group developed data definitions and agreed on whether it was mandatory (Level 1) or additional (Level 2). In total, 84 Level 1 and 79 Level 2 variables were selected for nine domains of HF care. These variables were reviewed by an international Reference Group with the Level 1 variables providing the dataset for registration of patients with HF on the EuroHeart IT platform. By means of a structured process and interaction with international stakeholders, harmonized data standards for HF have been developed. In the context of the EuroHeart, this will facilitate quality improvement, international observational research, registry-based randomized trials, and post-marketing surveillance of devices and pharmacotherapies across Europe.

Automatic Mining Method of Group Behavior Patterns Based on Incremental Spatiotemporal Trajectory Big Data

There is a problem of unclear group clustering in group behavior pattern mining, which leads to a long mining time. An automatic group behavior pattern mining method based on incremental spatiotemporal trajectory big data is proposed. The grid sequence of each road segment and the road segment information included in each grid are obtained using the group behavior pattern trajectory network. Using incremental trajectory data, the properties of incremental spatiotemporal trajectory big data are retrieved, and the group behavior pattern is grouped. In the obtained class, all data element records are categorized according to their data elements. Multiple attribute dimensions, such as data definition, limitations, and feature words, are used to standardize the spatiotemporal trajectory data pieces. To complete the autonomous mining of group behavior patterns, all subsequences are visited, computed, and compared. The test results show that when the group size threshold is 20, the running time of the group behavior pattern automatic mining method based on incremental spatiotemporal trajectory big data is 311.66, which is 141.29 s and 148.66 s shorter than that based on DBSCAN and K-means, respectively. Therefore, this method has higher execution efficiency.

Smart Tourism Recommendation Method in Southeast Asia under Big Data and Artificial Intelligence Algorithms

Smart tourism recommendation refers to supplementing suggestions for tourists’ self-service sightseeing plans based on Internet technology, and recommending more effective and practical tourism information. This paper uses big data and artificial intelligence algorithms to study the smart tourism recommendation method in Southeast Asia. First of all, this paper gives a brief overview of the definition and classification of big data and artificial intelligence algorithms and then introduces tourism resources and smart tourism in Southeast Asia. Finally, this paper conducts a comparative experiment between the smart tourism development model based on big data and artificial intelligence algorithms and the traditional tourism development model and analyzes the utility of the development model from three aspects, namely, tourists’ sense of experience and satisfaction, the scale of tourism transactions and the growth rate of tourism revenue, and the adequacy and harmony of tourism resource allocation. The final experimental results show that the overall average value of tourists’ experience and satisfaction under the smart tourism recommendation mode based on big data and artificial intelligence algorithms is 88.84 points, which is 7.30 points higher than the traditional tourism mode, which verifies its effectiveness.

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

BackgroundSuboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence.ObjectiveWe aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data.MethodsWe conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution).ResultsOur PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools.ConclusionsWe recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

Data standards for acute coronary syndrome and percutaneous coronary intervention: the European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart).

Standardized data definitions are essential for monitoring and benchmarking the quality of care and patient outcomes in observational studies and randomized controlled trials. There are no contemporary pan-European data standards for the acute coronary syndrome (ACS) and percutaneous coronary intervention (PCI). The European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart) project of the European Society of Cardiology (ESC) aimed to develop such data standards for ACS and PCI. Following a systematic review of the literature on ACS and PCI data standards and evaluation of contemporary ACS and PCI registries, we undertook a modified Delphi process involving clinical and registry experts from 11 European countries, as well as representatives from relevant ESC Associations, including the European Association of Percutaneous Cardiovascular Interventions (EAPCI) and Acute CardioVascular Care (ACVC). This resulted in final sets of 68 and 84 'mandatory' variables and several catalogues of optional variables for ACS and PCI, respectively. Data definitions were provided for these variables, which have been programmed as the basis for continuous registration of individual patient data in the online EuroHeart IT platform. By means of a structured process and the interaction with major stakeholders, internationally harmonized data standards for ACS and PCI have been developed. In the context of the EuroHeart project, this will facilitate country-level quality of care improvement, international observational research, registry-based randomized trials, and post-marketing surveillance of devices and pharmacotherapies.

Investigating Data Risk Considerations in Emergent Cyber Physical Production Systems

The Industrial Internet of Things (IIoT) describes a computing model where ubiquitous networks of heterogenous devices equipped with embedded sensors and actuators support innovative data-centric business models. Emergent IIoT use cases include Cyber Physical Production Systems (CPPS) to support asset optimization through self-organization of modular machines within production systems. In CPPS, raw materials, machines, and operations are interconnected to form a tightly integrated network. To ensure manufacturing continuity as CPPS networks evolve, asset managers will need to evaluate risk across multi-disciplinary domains. The domains have different architectures, lexicons, and priorities. To contribute to the eventual codification of data risk in CPPS, this research builds on previous literature to consider how data may traverse the CPPS model. The resulting models put forward in this research are informed by a transdisciplinary panel of experts drawn from disciplines including information and operational technology to bring greater specificity to the definition of business-critical data in supporting IIoT. Based on these expert views, a conceptual hierarchical automation architecture that may characterize many future state production processes is presented.

Effect of clinical versus administrative data definitions on the epidemiology of C. difficile among hospitalized individuals with IBD: a population-based cohort study

BackgroundHospitalization admissions and discharge databases (DAD) using the International Classification of Diseases (ICD) codes are often used to describe the epidemiology of Clostridioides difficile infections (CDI) among those with Inflammatory bowel disease (IBD), even though DAD CDI definition can miss many cases of CDI. There are no data comparing the assessment of the epidemiology of CDI among those with IBD by DAD versus laboratory diagnosis. We used a population-based dataset to determine the effect of using DAD versus laboratory CDI diagnosis on CDI assessment among those with IBD.MethodsWe linked the University of Manitoba IBD Epidemiology Database to the provincial CDI laboratory dataset for the years 2005–2014. Time trends of CDI were assessed using joinpoint analyses. We used stratified logistic regression analysis to assess factors associated with CDI among individuals with IBD.ResultsTime trends of CDI among hospitalized individuals with IBD were similar when using DAD or the laboratory CDI diagnosis. Prior hospital admission and antibiotic exposure were associated with CDI using either of the CDI definitions, 5-ASA use was associated with CDI using DAD but not laboratory diagnosis, whereas corticosteroid exposure was associated with laboratory-based CDI diagnosis. Using laboratory results as gold standard, DAD had a sensitivity and specificity of 75.4% and 99.6% for CDI among those with IBD.ConclusionsUsing ICD codes in the DAD for CDI provides similar epidemiological time trend patterns as identifying CDI in the laboratory dataset. Hence, ICD codes are reliable to determine CDI epidemiology among hospitalized individuals with IBD.

35 Using a New Quality Registry System to Improve Data Completeness for a National Burn Registry

IntroductionThe burn care community has demonstrated a long-standing commitment to quality of care, improved outcomes, and research by collecting and sharing clinical data through burn registries. The key to optimizing the value creation from these registries is data quality. In 2019 a new registry platform incorporating the latest, standardized, data definitions and sophisticated audit controls was piloted with 12 burn centers. The following year it was introduced to the broader burn care community along with a robust registrar education and training program. We sought to evaluate the effect of this new system on quality of data collection.MethodsWe compared data from 27 centers that collected data on the new system against their data collected on prior registry systems. We analyzed 26 data elements, across four different variable categories (Admissions (n=6), Demographics (n=5), Injury (n=10), and Outcomes(n=5)) that have been consistently collected over time, for data completeness. A two-proportion z test was used to assess statistical significance of differences in data completeness rates.ResultsThe 27-center cohort entered data on 4,524 inpatient burn admissions between January 1, 2020 and June 30th 2021 and 7,259 admissions between July 1st, 2019 and June 30th, 2020 in their legacy system. The data elements were harmonized to maximize longitudinal comparability.When comparing data from the same centers but using different registry software (graphic 1) 16 of 26 variables profiled had a higher percentage of completeness in the new registry system (all statistically significant), 5 variables were statistically significantly lower, and 5 variables were not significantly different. Overall, this subset of data elements showed a 14% improvement in data completeness between the new and legacy systems (90% vs 75%).ConclusionsThe development of a clinical registry requires significant commitment and leadership from the healthcare association and broader clinical community. The resources and effort that the burn community has expended to improve data quality has produced important gains in data quality with the introduction of a new, high-quality registry. The burn care community should continue to emphasize education, innovation, and collaboration to collect the highest quality data at the lowest burden from all burn centers.