Data Management Methods Research Articles

AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences

Early detection of heart failure (HF) can provide patients with the opportunity for more timely intervention and better disease management, as well as efficient use of healthcare resources. Recent machine learning (ML) methods have shown promising performance on diagnostic prediction using temporal sequences from electronic health records (EHRs). In practice, however, these models may not generalize to other populations due to dataset shift. Shifts in datasets can be attributed to a range of factors such as variations in demographics, data management methods, and healthcare delivery patterns. In this paper, we use unsupervised adversarial domain adaptation methods to adaptively reduce the impact of dataset shift on cross-institutional transfer performance. The proposed framework is validated on a next-visit HF onset prediction task using a BERT-style Transformer-based language model pre-trained with a masked language modeling (MLM) task. Our model empirically demonstrates superior prediction performance relative to non-adversarial baselines in both transfer directions on two different clinical event sequence data sources.

Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions

In the information age, “mobile Internet,” “cloud computing,” “Internet of Things,” and “data mining” concepts are emerging at the same time, as well as other fields of related data-based applications. The mobile application will be born as a result. Therefore, in the information age, big data, which involves information in a specific key or specialized field, has gradually begun to receive a lot of attention in recent years. In 2011, the US consulting firm McKinsey and Company first proposed the arrival of the “era of big data” and in August 2015 in China’s State Council issued a notice of action outline “to promote the development of big data.” Meanwhile, big data has gradually become an important factor in driving national reform and innovation, promoting scientific and technological progress, improving the way society is managed, and guiding changes in education and research. Big data is driving a very influential shift in thinking in an era where big data is changing the way we live, becoming the way we understand the world, and gradually becoming the source of new inventions and services. At the same time, the rapid development of big data technology for physical education teachers needs big data for management and training and other institutional managers to provide more effective ways and means of education management, but up to now, the status of big data for management is still another serious challenge, sports and training and other institutions of big data and processing process of data nonintelligent, nonclosed-loop processing, data nonlinked processing, etc. Many problems are also still very obvious. According to the new characteristics of sports big data refinement management, the current situation of sports professional training institutions teacher management, combined with sports training institutions to find some more practical sports training institutions teachers big data management methods can effectively improve the efficiency of management, teacher team building, strengthen sports training institutions to improve the quality of teaching teachers, and promote the overall quality of students have a positive impact. In this paper, we combine the characteristics of “big data” and the construction of teachers in sports training institutions, and put forward some suggestions on how to improve the level of teachers in sports training institutions in the era of big data and conclude that the construction of teachers in sports training institutions should seize the key era now and enter the “|big data era.” We conclude that the construction of teachers in sports training institutions should seize the critical era and enter the “big data era,” so as to rely on science and technology to improve the construction system of teachers in sports training institutions.

Heterogeneous Cluster Application Communication Optimization and Computer Big Data Management

In order to adapt to the constantly updated heterogeneous hardware and super-large-scale parallel computing environment and solve the problems of low programming level and difficult development, modification, and debugging of commonly used solutions, the author proposes a method for application communication optimization and computer big data management based on heterogeneous clusters. A new language mechanism is introduced to describe the multidimensional rule structure, arrangement, and communication mode of data and threads; and a software migration and optimization method between different types of heterogeneous systems based on the new language mechanism is proposed. And take direct method turbulence simulation as an example. Experimental results show that based on the Parray mechanism, it took only one week to complete the rapid migration of turbulence simulation applications on the Tianhe 1A system, and it was successfully run on a scale of 8192 cubic meters. Conclusion. The method realizes communication optimization and fast porting in different heterogeneous systems.

METHOD OF PERFORMING THE TRANSACTIONS ON MEDICAL DATA BASED ON BLOCKCHAIN TECHNOLOGIES

Today, healthcare decision-making processes are time consuming and complex. The productivity of health workers can be increased through the use of information technology. It is information technology that can reduce medical errors, provide more reliable solutions, and reduce health care costs. Today, medical information technology must meet 4 requirements: efficiency, safety, novelty and economic benefits. The healthcare sector in Ukraine has long been in need of change, and the many opportunities blockchain technology can help it lead the transformation of this area and meet these 4 requirements. Thus, increased attention to the technology of distributed registries has led to an understanding of the potential of blockchain technology in the health care system. Thus, the current challenge is the need to manage medical data based on blockchain technologies. The conducted state-of-the-art on known decisions on the use of blockchain technologies in the construction of information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Thus, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The aim of this study is to increase the efficiency of medical data management by developing a method of performing transactions on medical data based on blockchain technologies. A method of performing the transactions on medical data based on blockchain technologies has been developed, which consists of the stages of entering information into the blockchain and obtaining information from the blockchain. The developed method of performing the transactions on medical data provides: the ability to decide on the addition of transactions and medical data from the patient to the blockchain; the ability to decide on the issuance of data from the blockchain on request from a doctor

Considerations in releasing public data: The case of local governments in Korea

Local governments play a very important role in providing and disseminating public data; with the assistance of the national government, they also strive to promote effective policies for accessing this data. However, local governments face challenges in independently promoting public data policies owing to budget, manpower and technical constraints. This study analyses public data provided by local governments in South Korea from the perspective of management and use, apart from suggesting considerations for the public data policies of local governments. Public data provided by local governments were also collected, and both the data management method and use of such data were evaluated. Data management measures the currentness in complying with the update policy for each dataset, and the data utilisation measures the relationship between view and download as usefulness. A clustering analysis was conducted to analyse the common characteristics of individual local governments. According to the research results, most local governments do not systematically manage their datasets, and the use of the data provided is extremely low. It is therefore necessary to establish an effective data policy that considers the characteristics of local governments.

BIG DATA ANALYTICS: A LITERATURE REVIEW PAPER

In the digital era, decision makers have access to a large amount of data. As a result of the increasing volume and velocity of data in the world, conventional data management methods are no longer able to keep pace. As the amount of data grows, new ways to manage and extract value and information must be developed. There must be the ability to extract desired information from a broad variety of continually changing data, such as customer interactions and social network activity. Using the newest analytics methods on a vast amount of data, Big Data Analytics may provide such benefits.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Reaching the "Hard-to-Reach" Sexual and Gender Diverse Communities for Population-Based Research in Cancer Prevention and Control: Methods for Online Survey Data Collection and Management.

PurposeAround 5% of United States (U.S.) population identifies as Sexual and Gender Diverse (SGD), yet there is limited research around cancer prevention among these populations. We present multi-pronged, low-cost, and systematic recruitment strategies used to reach SGD communities in New Mexico (NM), a state that is both largely rural and racially/ethnically classified as a “majority-minority” state.MethodsOur recruitment focused on using: (1) Every Door Direct Mail (EDDM) program, by the United States Postal Services (USPS); (2) Google and Facebook advertisements; (3) Organizational outreach via emails to publicly available SGD-friendly business contacts; (4) Personal outreach via flyers at clinical and community settings across NM. Guided by previous research, we provide detailed descriptions on using strategies to check for fraudulent and suspicious online responses, that ensure data integrity.ResultsA total of 27,369 flyers were distributed through the EDDM program and 436,177 impressions were made through the Google and Facebook ads. We received a total of 6,920 responses on the eligibility survey. For the 5,037 eligible respondents, we received 3,120 (61.9%) complete responses. Of these, 13% (406/3120) were fraudulent/suspicious based on research-informed criteria and were removed. Final analysis included 2,534 respondents, of which the majority (59.9%) reported hearing about the study from social media. Of the respondents, 49.5% were between 31-40 years, 39.5% were Black, Hispanic, or American Indian/Alaskan Native, and 45.9% had an annual household income below $50,000. Over half (55.3%) were assigned male, 40.4% were assigned female, and 4.3% were assigned intersex at birth. Transgender respondents made up 10.6% (n=267) of the respondents. In terms of sexual orientation, 54.1% (n=1371) reported being gay or lesbian, 30% (n=749) bisexual, and 15.8% (n=401) queer. A total of 756 (29.8%) respondents reported receiving a cancer diagnosis and among screen-eligible respondents, 66.2% reported ever having a Pap, 78.6% reported ever having a mammogram, and 84.1% reported ever having a colonoscopy. Over half of eligible respondents (58.7%) reported receiving Human Papillomavirus vaccinations.ConclusionStudy findings showcase effective strategies to reach communities, maximize data quality, and prevent the misrepresentation of data critical to improve health in SGD communities.

Prediksi Penggunaan Obat Peserta Jaminan Kesehatan Nasional Menggunakan Algoritma Naïve Bayes Classifier

Currently, most of the patients seeking treatment at the hospital use the National Health Insurance (JKN) organized by the Healthcare and Social Security Agency (BPJS Kesehatan). In some hospitals, the figure is above 80%. Considering the very high number of BPJS Kesehatan participant seeking treatment at the hospital, a good data management method is needed, especially regarding the management of drug. Drug supply needs to be analyzed from time to time so that it can help predict future needs. An adequate supply of drugs and as needed is one of the things that affect service to patients. The availability of sufficient stock is expected to accelerate service to patients so that they do not have to wait long. Patients who are served quickly are expected to be satisfied. The impact of this patient satisfaction will increase the number of patient visits to the hospital. To support this, it is necessary to create a system that can estimate drug needs. The system can predict drug demand by using drug sales data to JKN participant patients for five years. Drug data used as research samples and then processed using an algorithm is the Naive Bayes Classifier. The Naive Bayes Classifier method is a method used to predict future opportunities using the basis of previous experience. A distinctive feature of this method is that it uses a very strong assumption of the independence of each event. While software testing uses the User Acceptance Test (UAT) model. Based on testing using this method, the system can be well received by users with a score of 78.64% (good).

MODEL AND METHOD OF ASSESSING THE MEDICAL DATA SUFFICIENCY

The conducted state-of-the-art on known decisions on the use of blockchain technologies in the construction of information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Thus, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The aim of this study is to improve the efficiency of medical data management by developing a method for assessing the sufficiency of medical data before placing them in the blockchain. Models of blockchain block, blockchain fragment and blockchain node, presented in formalized form, as well as models of medical data management process (data sufficiency assessment) are developed, which are the theoretical basis for developing a method of assessing the medical data sufficiency. Rules for determining the sufficiency of medical data have been developed., which provide analysis of medical data in order to find all the predetermined necessary elements of medical data; to decide on the sufficiency or insufficiency of information; output of visualized hints, which data elements are missing, to ensure the ability to quickly add data; quantitative assessment of the sufficiency of medical data. The paper developed a method for assessing the sufficiency of medical data, which provides: the ability to analyze a variety of medical data for their sufficiency; the ability to quantify the sufficiency of medical data; in case of insufficiency of medical data in the set, formation of recommendations on supplementing the set of medical data – as a guide, what information should be added to the set of medical data to be able to download them to the section “Block data” of blockchain.

Methods of medical data management based on blockchain technologies.

The healthcare sector in Ukraine has long been in need of change, and many opportunities of blockchain technology can help it lead the transformation of this sector and ensure compliance with the requirements of the efficiency, safety, novelty, and economic benefits. State-of-the-art on known decisions on the use of blockchain technologies in the information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Therefore, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The paper developed a method for assessing the sufficiency of medical data, which provides: the ability to analyze a variety of medical data for their sufficiency; the ability to quantify the sufficiency of medical data; in case of insufficiency of the medical data, formation of recommendations on supplementing the medical data to be able to upload them to the "Block Data" section of the blockchain block. The paper developed a method of performing transactions on medical data, which consists of the stages of entering information into the blockchain and obtaining information from the blockchain. The developed method of performing transactions on medical data provides the ability to decide on the addition of transactions and medical data from the patient to the blockchain; the ability to decide on the issuance of data from the blockchain on request from a doctor.

Discussion on Continuing Education of College Accountants under the Background of Big Data Era

In recent years, big data technology has gradually entered people's lives. All walks of life use big data technology to break the traditional data management method. The scale of data and the type of data processing are more diversified. The application makes the work of accountants more convenient, and also provides a broad space for the reform of accounting education. This paper takes the new characteristics of college accounting continuing education under the background of big data technology as the breakthrough point, expounds the problems existing in accounting continuing education under the background of big data, and puts forward specific measures to strengthen accounting continuing education, aiming to provide continuing education and development assistance for college accountants.

B-EagleV: Visualization of Big Point Cloud Datasets in Civil Engineering Using a Distributed Computing Solution

Point cloud data (PCD) have attracted attention in many disciplines, including civil engineering. However, big PCD have posed great challenges for conventional approaches using a single computer. Although many published studies have demonstrated distributed computing’s potential for large-scale data-intensive applications, this technology has not been applied widely in processing of big PCD due to a lack of methods for data management, visualization, and analysis. To strengthen the foundation of distributed computation in civil engineering, this study offers a solution to one of the obstacles presented in the previous studies, which was the visualization of big PCD. The practical result of this study is the introduction of B-EagleV, a cost-effective Hadoop-based solution for the visualization of big PCD in civil engineering with almost complete components of scalable storage, high-performance rendering, and interactive visualization. Through experiment results and demonstration, B-EagleV showed great promise for data management, progress monitoring, and survey conduction in the construction sector.

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review.

BackgroundPreterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).ObjectiveThis study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.MethodsThis systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported.ResultsA total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.ConclusionsVarious ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.

Risk Management of New Cosmetic Product Development Based on Data Management of Visualization in Scientific Computing

With the continuous development of the social economy, people pay more and more attention to their own protection and maintenance, especially the quality of commodities in terms of usage of the commodity. As commonly used commodities, the quality of their new products in cosmetics is extremely important. In the rapidly developing cosmetics industry, in view of these needs and limitations, the data management method of visualization in scientific computing is introduced in this paper. Multiple risk factors in cosmetics development are established and sorted from the three perspectives of cosmetics development, production, and sales, by combining the new cosmetic product development process. The biggest risks are clarified, the corresponding cosmetic product risk assessment model is constructed, and the new cosmetic product development risk strategy and related management are realized. The simulation experiment results show that the data management method of visualization in scientific computing is effective and can support the risk management of new cosmetic product development.

ASSESSMENT OF GROUNDWATER POTENTIAL AND AQUIFER VULNERABILIY USING MCDA AND DRASTIC ANALYSIS WITHIN AKURE METROPOLIS, SOUTHWESTERN NIGERIA

Groundwater potential and aquifer vulnerability in some part of Akure metropolis southwestern Nigeria was carried out, using electrical resistivity survey, to identify and determine possible areas for groundwater development and its aquifer vulnerability. Geoelectric parameters from electrical resistivity survey helped in characterizing, assessing and evaluating the hydrogeological condition of aquifers in the area. A way of integrating all aquifer parameters significant to evaluating the groundwater potential and vulnerability, using the principle of Drastic and Multi-Criteria Evaluation Techniques, was proposed. The investigated area was classified into low, moderate and high groundwater prospect and aquifer vulnerability using the proposed method. The approach allows the weighting and integrating of all aquifer parameters in order of their significance to aquifer vulnerability. Integration of geophysical method with the proposed data management method provided a cost effective, dependable and accurate characterization and evaluation of an aquifer system.

E GAS SEWA

To cater to users, a gas agency need efficient data management and customer management methods. Every day, there will be numerous requests from users in various locations that must be kept in a database and supplied with a delivery date and time by which they must be submitted. We need an automated system to manage these tasks. This application, e gas sewa, will assist gas agencies in managing additions in a short amount of time with minimal manual effort. The following are some of the project's most critical tasks: The user orders gas online, the administrator searches for users, the administrator issues dealerships, the dealer sees, and the consumer information. Cancellation of the reservation

Integration of an AI-Based Platform and Flipped Classroom Instructional Model

This article aims to evaluate a flipped class teaching model based on an AI (artificial intelligence) Language Learning Platform. The platform adopts mainstream collaborative filtering and BPMF (Bayesian Probabilistic Matrix Factorization) and big data management methods to provide students’ detailed performance analysis reports and push recommendation exercises. The researcher reconciled the flipped teaching model with the AI-based language learning platforming in an experimental class and conventionally in a control class. At the end of the semester, the researcher compared students’ performance as well as their attitudes toward the course between the two classes through a postexam paper and a questionnaire. Combining quantitative and qualitative methods, the survey shows that although the AI-based language learning mode failed to significantly increase students’ English listening scores, compared with the students in the traditional class, those receiving the new mode held a more positive attitude toward their English listening experience, especially in regard to learning interest, study autonomy, and class involvement. The flipped class mode not only relieved students’ partial cognitive processing workloads in class, but also boosted their confidence to engage in classroom discussion by reducing their nervousness through before-class preview. The AI-based language learning platform mainly performs monitoring and tutoring functions which facilitate the flipped class instruction. This study proposes a new flipped teaching model and proves it effective, indicating that for the flipped class learning mode to be successful, it is of importance to integrate face-to-face classroom instruction with the AI-technology-assisted online learning experience in a way that they can coherently support each other.

Data management in diabetes clinical trials: a qualitative study

BackgroundClinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.MethodThis was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.ResultsThe five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.ConclusionAccording to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.

Early Undergraduate Biostatistics & Data Science Introduction Using R, R Studio & the Tidyverse

Increasingly, students training in the biological sciences depend on a proper grounding in biological statistics, data science and experimental design. As biological datasets increase in size and complexity, transparent data management and analytical methods are essential skills for undergraduate biologists. We propose that using the software R and RStudio are effective tools to train first- and second-year undergraduate students in data visualization and foundational statistical analyses. Here, we present the redesigned laboratory curriculum for our Experimental Design and Statistics course, a required course for all first- or second-year biology majors at Lawrence University, a small liberal arts institution in northeast Wisconsin. We include an example 10-week syllabus and eight laboratory exercises (as supplementary materials) for undergraduate institutions that aim to introduce and guide students through acquiring a basic understanding of biostatistical analyses and skills using R and RStudio. We also provide a flexible framework and examples that are easily modifiable and cover the essential biostatistics and data science skills needed for biology undergraduates. Finally, we discuss the potential pitfalls and obstacles as well as the intrinsic benefits and expected outcomes of our laboratories.