• Journal of Multimedia Information System
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• v.5 no.3
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• pp.171-178
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• 2018

According to the National Health Insurance Corporation in 2008, there were 17,764,428 physical therapy patients, exceeding 31 percent for the population covered by health insurance. This means that three out of 10 Koreans received physical therapy. And now, 10 years later, due to the aging population and the increase in the sports population, the number of patients with physical therapy is expected to be much more than a decade ago. Among them, many physical therapy patients were orthopedic and neurologic disorder. However, in the medical field applied to physical therapy, it is widely applied across all medical fields, including orthopedics, neurosurgery, pediatrics, gynecology, thoracic surgery and dentistry. It is believed that various cases of patients receiving physical therapy will be secured. as mentioned earlier, there will be a large number of patients with physical therapy treatments, making big data analytics easier. based on this, physical therapy applications are thought to be helpful in the analogy of disease and the development of effective physical therapy and will ultimately promote the development of physical therapy.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.107-114
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• 2020

This paper studies a method for estimating the scale of a Hadoop cluster to process big data as a cost-effective manner. In the case of medical institutions, demands for cloud-based big data analysis are increasing as medical records can be stored outside the hospital. This paper first analyze the Amazon EMR framework, which is one of the popular cloud-based big data framework. Then, this paper presents a efficiency model for scaling the Hadoop cluster to execute a Mapreduce application more cost-effectively. This paper also analyzes the factors that influence the execution of the Mapreduce application by performing several experiments under various conditions. The cost efficiency of the analysis of the big data can be increased by setting the scale of cluster with the most efficient processing time compared to the operational cost.

• Convergence Security Journal
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• v.16 no.7
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• pp.21-29
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• 2016

Our society is currently exposed to environment of various information that is exchanged real time through networks. Especially regarding medical policy, the government rushes to practice remote medical treatment to improve the quality of medical services for citizens. The remote medical practice requires establishment of medical information based on big data for customized treatment regardless of where patients are. This study suggests establishment of regional medical cluster along with defense and protection cooperation models that in case service availability is harmed, and attacks occur, the attacks can be detected, and proper measures can be taken. For this, the study suggested forming networks with nationwide local government hospitals as regional virtual medical cluster bases by the same medical information system. The study also designed a mutual cooperation security model that can real time cope with IP Spoofing attack that can occur in the medical cluster and DDoS attacks accordingly, so that the limit that sole system and sole security policy have can be overcome.

• The Journal of Information Systems
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• v.31 no.2
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• pp.217-238
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• 2022

Purpose This study aims to develop a work-related injury and illness monitoring geographic information system that analyzes and visualizes the types of work-related injury and illness based on workers' compensation insurance big data. Design/methodology/approach Using the developed system, we explained the process of monitoring the areas of the applied workplace, medical care application, index, and medical care institution. We also showed examples of analyzing the index and medical care institution area. By applying the system, we can intuitively recognize the current status of workers' compensation insurance and confirm the basic information necessary for managing the current status of workers' compensation insurance. Findings We generated more helpful information by combining workers' compensation insurance data and designated medical care institution data. We were able to apply the severity score and the vulnerability index of work-related injury and illness to the system as a demonstration. To efficiently manage workers' compensation insurance, it was necessary to integrate workers' compensation insurance and designated medical care institution data, as well as the data from various sources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.835-841
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• 2018

In this study, we analyzed public health data analysis using the hadoop-based spack in the cloud environment using the data of the Community Health Survey from 2012 to 2014, and the factors affecting the quality of life and quality of life. In the proposed paper, we constructed a cloud manager for parallel processing support using Hadoop - based Spack for open medical big data analysis. And we analyzed the factors affecting the "quality of life" of the individual among open medical big data quickly without restriction of hardware. The effects of public health data on health - related quality of life were classified into personal characteristics and community characteristics. And multiple-level regression analysis (ANOVA, t-test). As a result of the experiment, the factors affecting the quality of life were 73.8 points for men and 70.0 points for women, indicating that men had higher health - related quality of life than women.

• Industry Promotion Research
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• v.7 no.3
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• pp.17-26
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• 2022

As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.483-486
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• 2021

With the explosive increase in data, the 'big data era' has arrived, focusing on deriving new values and insights through data. With the development of data analysis technology, the importance of data analysis and utilization in the field of diagnosis and treatment as well as prevention is expanding, while the use of big data is emerging in the healthcare field. Moreover, as the three data-related laws (Personal Information Protection Act, Information and Communication Network Act, and Credit Information Act) were passed in January 2020, it became possible to use a wide range of big data through pseudonym information. However, the use of healthcare big data is still struggling due to various policies and regulations, inconsistent data quality, and the absence of specialized personnel. Therefore, in this study, examines the current state of use of big data in the healthcare field, and analyzes the challenges, overseas cases, plans, and expected effects for activation of healthcare big data.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.207-212
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• 2018

As a data analysis tool which is becoming popular in the Big Data era, R is rapidly expanding its user range by providing powerful statistical analysis and data visualization functions. Major advantage of R is its functional scalability based on open source, but its scale scalability is limited, resulting in performance degrades in large data processing. RHadoop, one of the extension packages to complement it, can improve data analysis performance as it supports Hadoop platform-based distributed processing of programs written in R. In this paper, we evaluate the validity of RHadoop by evaluating the performance improvement of RHadoop in real medical big data analysis. Performance evaluation of the analysis of the medical history information, which is provided by National Health Insurance Service, using R and RHadoop shows that RHadoop cluster composed of 8 data nodes can improve performance up to 8 times compared with R.

• Progress in Medical Physics
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• v.30 no.2
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.