• Journal of Korean Medicine for Obesity Research
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• v.17 no.1
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• pp.46-53
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• 2017

Health care big data is thought to be a promising field of interest for disease prediction, providing the basis of medical treatment and comparing effectiveness of different treatments. Korean government has begun an effort on releasing public health big data to improve the quality and safety of medical care and to provide information to health care professionals. By studying population based big data, interesting outcomes are expected in many aspects. To initiate research using health care big data, it is crucial to understand the characteristics of the data. In this review, we analyzed cases from inside and outside the country using clinical data registry. Based on successful cases, we suggest research method for evidence-based Korean medicine. This will provide better understanding about health care big data and necessity of Korean medicine data registry network.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.325-340
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• 2020

Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.397-407
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• 2014

Due to rapid development of medical information, a huge amount of information is being accumulated. Desires to conduct clinical researches by using this information are increasing, and medical institutions are encountering problems of aging society and drastic increase of medical expenses. Utilization of Big Data as an alternative is now being emphasized. The purpose of this study is to examine informatization of medical institutions and suggest political implications for Big Data utilization plans. Data was collected through literature searches and interviews with medical information professionals of medical institutions, from September to November, 2013, for four months. As a result of the study, it could be found that the hospital information system is improving from patient management and administration to researches and information strategies. Thus, national supports for medical expense reduction as well as fostering professional manpower should be provided, considering establishment of the system for utilization of Big Data and efficient application of unstructured data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.728-731
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• 2014

Today, the quality of medical service has become a major concern because that sustainable development of IT technology and extending people's life expectancy. This paper, it is used as a tool for the medical information quality management that analyze tweets big data form generated by individual's daily. The result of the analyze big data offers improvement medical information based evidence based medicine. Also it has been possible for a trace observation of chronic disease and can reduce additional other complications of patients. Therefore, effective treatment of disease and prevention is possible.

• Mycobiology
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• v.49 no.6
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• pp.582-588
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• 2021

The interaction of mating pheromone and pheromone receptor from the B mating-type locus is the first step in the activation of the mushroom mating signal transduction pathway. The B mating-type locus of Lentinula edodes is composed of Bα and Bβ subloci, each of which contains genes for mating pheromone and pheromone receptor. Allelic variations in both subloci generate multiple B mating-types through which L. edodes maintains genetic diversity. In addition to the B mating-type locus, our genomic sequence analysis revealed the presence of a novel chromosomal locus 43.3 kb away from the B mating-type locus, containing genes for a pair of mating pheromones (PHBN1 and PHBN2) and a pheromone receptor (RCBN). The new locus (Bα-N) was homologous to the Bα sublocus, but unlike the multiallelic Bα sublocus, it was highly conserved across the wild and cultivated strains. The interactions of RcbN with various mating pheromones from the B and Bα-N mating-type loci were investigated using yeast model that replaced endogenous yeast mating pheromone receptor STE2 with RCBN. The yeast mating signal transduction pathway was only activated in the presence of PHBN1 or PHBN2 in the RcbN producing yeast, indicating that RcbN interacts with self-pheromones (PHBN1 and PHBN2), not with pheromones from the B mating-type locus. The biological function of the Bα-N locus was suggested to control the expression of A mating-type genes, as evidenced by the increased expression of two A-genes HD1 and HD2 upon the treatment of synthetic PHBN1 and PHBN2 peptides to the monokaryotic strain of L. edodes.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.87-95
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• 2022

Recently, with the development of smart technology, in medical information platform, patient's biometric data is measured in real time and accumulated into database, and it is possible to determine the patient's emergency situations. Medical staff can easily access patient information after simple authentication using a mobile terminal. However, in accessing medical information using the mobile terminal, it is necessary to study authentication in consideration of the patient situations and mobile terminal. In this paper, we studied on medical information platforms based on big data processing and edge computing for supporting automatic authentication in emergency situations. The automatic authentication system that we had studied is an authentication system that simultaneously performs user authentication and mobile terminal authentication in emergency situations, and grants upper-level access rights to certified medical staff and mobile terminal. Big data processing and analysis techniques were applied to the proposed platform in order to determine emergency situations in consideration of patient conditions such as high blood pressure and diabetes. To quickly determine the patient's emergency situations, edge computing was placed in front of the medical information server so that the edge computing determine patient's situations instead of the medical information server. The medical information server derived emergency situation decision values using the input patient's information and accumulated biometric data, and transmit them to the edge computing to determine patient-customized emergency situation. In conclusion, the proposed medical information platform considers the patient's conditions and determine quick emergency situations through big data processing and edge computing, and enables rapid authentication in emergency situations through automatic authentication, and protects patient's information by granting access rights according to the patient situations and the role of the medical staff.

• Smart Media Journal
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• v.5 no.4
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• pp.124-130
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• 2016

Big data technologies are increasing the need for big data in many areas of the world. Recently, the health care industry has become increasingly aware of the importance of disease and health care services, as it has become increasingly immune to prevention and health care. To do this, we need a Big data system to collect and analyze the personal biometric data. In this paper, we design the biometric big data system using low cost wearable device. We collect basic biometric data, such as heart rate, step count and physical activity from Mi Band, and store the collected biometric data into MongoDB. Based on the results of this study, it is possible to build a big data system that can be used in actual medical environment by using Hadoop etc. and to use it in real medical service in connection with various wearable devices for medical information.

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