• Healthcare Informatics Research
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• 제24권4호
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• pp.251-252
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• 2018

• 한국병원경영학회지
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• 제23권4호
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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.

• 한국컴퓨터정보학회논문지
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• 제19권12호
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• pp.101-109
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• 2014

의학기술의 발전과 소득수준의 증가로 "건강하게 오래살기"에 관심이 높아지면서 적극적으로 건강을 증진하고 유지하는 웰니스가 확대되고 있다. 또한 맞춤형 의료서비스에 대한 수요가 증가하고 방대한 의료 빅 데이터를 이용한 질병 예방의 움직임도 나타나고 있다. 이 논문에서는, 의료 시장에서 주요 관심분야로 부각되고 있는 웰니스를 지원하기위해 빅 데이터 기반의 의료 질 향상을 통한 환자중심의 의료서비스를 목적으로 한다. 환자를 약물에 의존적으로 치료만 하는 것이 아니라 식생활 개선을 기반으로 질병예방과 치료를 위해 빅데이터를 분석한다. 개인 트윗터를 분석해서 일상생활정보를 획득하고 웰니스 사전을 기반으로 질병예방과 치료를 목적으로 한다. 효율적인 빅데이터 분석을 위해 하둡노드를 증가하면서 데이터 처리시간을 실험하였다. 실험결과 저장시간의 경우 63%, 데이터 통합의 경우 18%, 전체 테스트 시간을 기준으로 26%로 하나의 노드로 처리하는 경우보다 세 개의 노드로 처리하는 것이 효율적임을 실험을 통해 확인하였다.

• 한국지능시스템학회논문지
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• 제23권5호
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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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• 제49권3호
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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.

• 한국산업융합학회 논문집
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• 제25권6_2호
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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.

• 한국콘텐츠학회논문지
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• 제18권9호
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• pp.149-155
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• 2018

의료정보의 급속한 발달로 인하여 막대한 의료 빅데이터 정보가 축적되고 있다. 이러한 의료 빅데이터는 고령화와 의료비의 가파른 상승을 해결하는데 중요한 자료로 활용될 가능성이 큰 분야이다. 특히 한국의 경우 전 국민대상 주민등록번호와 전산화된 이용 자료들을 기반으로 빅데이터를 활용할 수 있는 의료 인프라 측면에서는 다른 국가에 비해 우위를 점할 수 있다. 본 연구에서는 국민건강보험공단 빅데이터 자료와 통계청 사망자 데이터를 활용하여 강원도의 사망자 분석 및 사망률에 영향을 미치는 요인을 분석하기 위해 상관분석을 수행한 후 시사점을 도출하였다. 분석 결과, 강원도 사망률과 관계된 주요 변수로는 자기지역 병원 인프라 이용률, 소득수준, 고령화 및 인구 밀도 등으로 나타났다. 따라서 소득격차에 따른 불평등과 지역 의료 인프라 부족 등은 지역의 사망률에 부정적 영향을 미치고 있는 것으로 나타나고 있어, 지역 병원 인프라 개선 및 소득 수준 향상을 위한 지역별 맞춤형 정책지원이 필요하다. 본 연구의 결과는 의료 빅데이터를 활용하여 강원도민 사망자를 분석하고, 다양한 사회 지표와 상관분석을 통해 그 원인을 파악해 보았다는 점에서 연구의 의의가 있다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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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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• 제5권1호
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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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• 제16권6호
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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.