• Healthcare Informatics Research
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• v.24 no.4
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• pp.251-252
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• 2018

• Korea Journal of Hospital Management
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• v.23 no.4
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• pp.26-39
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• 2018

Purposes: The purpose of this study is to analyze the issue of interest in patient medical service of small and medium hospitals using big data. Methods: The method of this study was implemented by data mining and social network using SNS big data. The analysis tool were extracted key keywords and analyzed correlation by using Textom, Ucinet6 and NetDraw program. Findings: In the results of frequency, the network-centered and closeness centrality analysis, It was shown that the government center is interested in the major explanations and evaluations of the technology, information, security, safety, cost and problems of small and medium hospitals, coping with infections, and actual involvement in bank settlement. And, were extracted care for disabilities such as pediatrics, dentistry, obstetrics and gynecology, dementia, nursing, the elderly, and rehabilitation. Practical Implications: Future studies will be more useful if analyzed the needs of customers for medical services in the metropolitan area and provinces may be different in the small and medium hospitals to be studied, further classification studies.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.101-109
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• 2014

Medical technology development and increase the income level of a "Long and healthy Life=Wellness," with the growing interest in actively promoting and maintaining health and wellness has become enlarged. In addition, the demand for personalized health care services is growing and extensive medical moves of big data, disease prevention, too. In this paper, the main interest in the market, highlighting wellness in order to support big data-driven healthcare quality through patient-centered medical services purposes. Patients with drug dependence treatment is not to diet but to improve disease prevention and treatment based on analysis of big data. Analysing your Tweets-daily information and wellness disease prevention and treatment, based on the purpose of the dictionary. Efficient big data analysis for node while increasing processing time experiment. Test result case of total access time efficient 26% of one node to three nodes and case of data storage is 63%, case of data aggregate is 18% efficient of one node to three nodes.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

• Mycobiology
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• v.49 no.3
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• pp.280-284
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• 2021

Mushroom strains of Polyporales from the genera Coriolus, Trametes, Pycnoporus, Ganoderma, and Formitella were explored in terms of mycelial growth characteristics for the application of mushroom mycelia as alternative sources of materials replacing fossil fuel-based materials. Among the 64 strains of Polyporales, G. lucidum LBS5496GL was selected as the best candidate because it showed fast mycelial growth with high mycelial strength in both the sawdust-based solid medium and the potato dextrose liquid plate medium. Some of the Polyporales in this study have shown good mycelial growth, however, they mostly formed mycelial mat of weak physical strength. The higher physical strength of mycelial mat by G. lucidum LBS5496GL was attributed to its thick hyphae with the diameter of 13 mm as revealed by scanning electron microscopic analysis whereas the hyphae of others exhibited less than 2 mm. Glycerol and skim milk supported the best mycelial growth of LBS5496GL as a carbon and a nitrogen source, respectively.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.1083-1090
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• 2022

Since renal cell carcinoma(RCC) has various examination and treatment methods according to clinical stage and histopathological characteristics, it is required to determine accurate and efficient treatment methods in the clinical field. However, the process of collecting and processing RCC medical data is difficult and complex, so there is currently no AI-based clinical decision support system for RCC treatments worldwide. In this study, we propose a clinical decision support system that helps clinicians decide on a precision treatment to each patient. RCC standard big database is built by collecting structured and unstructured data from the standard common data model and electronic medical information system. Based on this, various machine learning classification algorithms are applied to support a better clinical decision making.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.149-155
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• 2018

Due to the rapid development of medical information, vast amounts of medical data are accumulating, and such medical data is highly likely to be used as an important data for solving the aging population and the rapid rise in medical cost. Especially in Korea, there are resident registration numbers and computerized usage data for all citizens, so it can be superior to other countries in terms of medical infrastructure that can utilize big data. The purpose of this study was to analyze the factors affecting the mortality and death rate of Gangwon using the Big Data and the National Statistical Office data centered on Kangwon province. As a result of analysis, major variables related to the mortality rate of Gangwon were hospital infrastructure utilization rate, income level, aging population and population density. Therefore, inequalities due to income disparities and insufficient local medical infrastructures were affecting the local mortality rate, and policy support was needed to improve the local hospital infrastructure and income level. The results of this study were meaningful in that medical big data were used to analyze the deaths of people in Gangwon, and the causes of the deaths were analyzed through various social indicators and correlation analysis.

• Annual Conference of KIPS
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• 2017.04a
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• pp.540-543
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• 2017

It will be possible to solve some of the major issues in our society and economy with the emerging Big Data used across 21st century global digital economy. One of the main areas where big data can be quite useful is the medical and health area. IT technology is being used extensively in this area and expected to expand its application field further. However, there is still room for improvement in the usage of Big Data as it is difficult to search unstructured data contained in Big Data and collect statistics for them. This limits wider application of Big Data. Depending on data collection and analysis method, the results from a Big Data can be varied. Some of them could be positive or negative so that it is essential that Big Data should be handled adequately and appropriately adapting to a purpose. Therefore, a Big Data has been constructed in this study to applying Crawling technique for data mining and analyzed with R. Also, the data were visualized for easier recognition and this was effective in developing an individualized health plan from different angles.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.47-50
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• 2016

Big Data is coming rapidly in recent times and keep the vast amount of data was utilized them. These data are utilized in many fields in particular, based on the patient data in the medical field to increase the therapeutic effect, as well as re-incidence to better treatment, lowering the readmission rates increased the quality of life. In this paper it is practiced to report basis of the analysis and verification of data using python. And it can be analyzed the data through a simple formula, from Select reason of Python to how it used; by Press analysis of Agriculture, Forestry and Fisheries research. In this process, a simple formula can be used that expression for analyzing the actual data so it taking advantage of the use of functions in real life.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.