• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.25-31
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• 2015

Smart healthcare environments which merge medical and IT technology are getting ready for the third generation centering EHR from current second generation. As a basic technology for the introduction and activation of EHR systems it requires heterogeneous network interworking techniques between various wired and wireless medical devices. Interworking technology for heterogeneous network among various medical devices is needed to introduce EHR system. The heterogeneous network interworking technology is needed for construction of a reliable data system to convert each of unstructured data into structured data. Therefore, in this paper, we identify the domestic and international trends of smart medical field and analyze the characteristics of wired and wireless communication technology that is used in a heterogeneous network. and also suggest requirements needed for interworking technology and provide interworking technology based on them. we expect that proposed method which is designed for smart healthcare environments would provide a basic architecture needed for third smart medical technology generation.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.75-80
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• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• Knowledge Management Research
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• v.19 no.4
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• pp.149-170
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• 2018

Healthcare lifelog, a personal record relating to disease treatment and healthcare, plays an important role in healthcare paradigm shifts in which medical and information technology converge. Healthcare services based on various healthcare lifelogs are being launched domestically by both large corporations and small and medium enterprises, however, they are being built on an individual platform that is dependent on each company. Therefore, the terms of lifelog data are different as well as the measurement specifications are not uniform. This study proposes a reference model for minimum common data required for sharing and utilization of healthcare lifelog. Literature study and expert survey derived 3 domain, 17 essential items, and 51 sub-items. The model provides definition, measurement data format, measurement method, and precautions for each detailed measurement item, and provides necessary guidelines for data and service design and construction for healthcare service. This study has its significance as a basic research supporting the activation of ecosystem by ensuring interoperability of data between heterogeneous healthcare devices linked to digital healthcare platform.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.414-415
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• 2011

Due to heterogeneity in Data and Processes, healthcare systems are facing the challenge of interoperability. This heterogeneity results in different healthcare workflows of each individual organization. The compatibility of these heterogeneous workflows is possible when standards are followed. HL7 is one of the standards that is used for communicating medical data between healthcare systems. Its newer version V3 is providing semantic interoperability which is lacking in V2. The interoperability in HL7 V3 is only limited to data level and process level interoperability needs to be catered. The process level interoperability is achieved only when heterogeneous workflows are aligned. These workflows are very complex in nature due to continuous change in medical data resulting in problems related to maintenance and degree of automation. Semantic technologies plays important role in resolving the above mentioned problems. This research work is based on the integration of semantic technology in HL7 V3 standard to achieve semantic process interoperability. Web Service Modeling Framework (WSMF) is used for incorporating semantics in HL7 V3 processes and achieves seamless communication. Interaction Ontology represents the process artifacts of HL7 V3 and helps in achieving automation.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.88-92
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• 2015

Today, the medical information system has evolved in the way of integrated healthcare IT information systems. Therefore, it is trying to build advanced U-Healthcare service. Though the U-Healthcare environments is exchanged the information between systems in many cases, however since the each system is different, the integration and exchange of data is difficult. To overcome this problem, in this paper it proposes that we suggests a possible DBaaS(DataBase as a Service) for the heterogeneous integration of medical information management and data exchange. First, the proposed system builds DBaaS cloud by integrating the meta-DB Schema level and DB Schema for each hospital. And, the mapping the schema data and the existing hospital information system is possible using the International Standard HL7. By applying the proposed method to the hospital system, it comes true the efficient exchange of information between the patients, doctors, staffs through the data mapping of the one to multi-system.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.91-97
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• 2017

IoT (Internet of Things) systems are based on heterogeneous hardware systems of different types of devices interconnected each other, ranging from miniaturized and low-power wireless sensor node to cloud servers. These IoT systems composed of heterogeneous hardware utilize data sets collected from a particular set of sensors or control designated actuators when needed using open APIs created through abstraction of devices' resources associated to service applications. However, previously existing IoT services have been usually developed based on vertical platforms, whose sharing and exchange of data is limited within each industry domain, for example, healthcare. Such problem is called 'data silo', and considered one of crucial issues to be solved for the success of establishing IoT ecosystems. Also, IoT services may need to dynamically organize their services according to the change of status of connected devices due to their mobility and dynamic network connectivity. We propose a way of dynamically composing IoT services under the concept of WoT (Web of Things) where heterogeneous devices across different industries are fully integrated into the Web. Our approach allows developers to create IoT services or mash them up in an efficient way using Web objects registered into multiple standardized horizontal IoT platforms where their resources are discoverable and accessible. A Web-based service composition tool is developed to evaluate the practical feasibility of our approach under real-world service development.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.474-475
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• 2013

In healthcare systems, the accuracy of a classifier for classifying medical diseases depends on a reduced dataset. Key to achieve true classification results is the reduction of data to a set of optimal number of significant features. The initial step towards data reduction is the integration of heterogeneous data sources to a unified reduced dataset which is further reduced by considering the range of values of all the attributes and then finally filtering and dropping out the least significant features from the dataset. This paper proposes a three step data reduction model which plays a vital role in the classification process.