• The Journal of Information Systems
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• v.16 no.2
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• pp.93-122
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• 2007

RFID has fundamental influences on today's business management. This research seeks to formulate the opportunities and challenges, the strengths and weaknesses and the perceived benefits of RFID implementation in three industries: manufacturing, medical-service, and distribution. Ten companies of successful RFID deployment in Korea are presented. Field interview and panel discussion were used to explore the research purpose. The core challenges include RFID readability issues, lack of best practices, increasing prime cost, visible ROI, implementation cost, and employee's resistance. The strengths include IT infrastructure, system integration competency, RFID business model creation ability, executive's support. There is little weakness in Korea companies, there are many perceived benefits in three industries. The most distinctive finding is that the visible ROI was found out in the manufacturing industry, not in the distribution industry. The opportunities and challenges, the strengths and weaknesses and the perceived benefits are some different in three industries, which provide valuable guidance for Korean companies in seeking the RFID opportunities. This case study represents a pioneer research of RFID adoption in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.776-779
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• 2010

Implementation for Gated Peak Detector CSAM (C Scanning Acoustic Microscope) based on One Chip processor is proposed in this paper. GDP (Gated Peak Detector) is implemented with VHDL tool. The proposed method leads to be available for its application and integration in all systems as well as acoustic microscope and the method is compared with the conventional methods. The technique results in efficiency in size and application.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.522-538
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• 2001

This paper presents a cal center which is a subsystem of a web-based real time monitoring system of intensive care units. Based on Computer-Telephony Integration (CTI) technology, the call center attempts to efficiently and automatically send messages to patients\` families, doctors, and other staffs in hospital via communication media suitable to the occasion. The problem of determining appropriate media can be very complicated by the urgency of a message, calling time, and communication media available to the target person. We use the Dempster-Shafer theory, one of the uncertainty handling methods, to determine the most suitable communication media that will transmit a message rapidly and safely. In addition, we use agent technology to perform the calling process without requiring the intervention of the user of the call center. this call center enables message transfer through various communication media in an integrated environment, and relieves medical staff from the calling process, which in turn will make a contribution toward enhancing medical service.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.27-35
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• 2009

A paradigm of medical industry is changing quickly to u-healthcare according to entry toward an aging society and improvement of quality of life(QoL). The change toward u-healthcare is meaningful since meaning of healthcare is redefined by prevention and management instead of medical service such as diagnosis of disease and treatment. However, the interest about u-healthcare is only concentrated to derivation of new healthcare service, development of medical measurement appliances(Sensors), and integration and standardization of medical information. Therefore, in this paper, the main ai of this study is trying to realize and implement u-healthcare technology through primary philosophies of ubiquitous composition such as Disappear Computing, Invisible Computing, and Calm Computing and development of user-centered technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1830-1840
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• 2011

CDISC (Clinical Data Interchanging Standards Consortium) standards are to support the acquisition, exchange, submission and archival of clinical trial and research data. SDTM (Study Data Tabulation Model) for Case Report Forms (CRFs) was recommended for U.S. Food and Drug Administration (FDA) regulatory submissions since 2004. Although the SDTM Implementation Guide gives a standardized and predefined collection of submission metadata 'domains' containing extensive variable collections, transforming CRFs to SDTM files for FDA submission is still a very hard and time-consuming task. For addressing this issue, we developed metadata based SDTM mapping rules. Using these mapping rules, we also developed a semi-automatic tool, named CDISC Transformer, for transforming clinical trial data to CDISC standard compliant data. The performance of CDISC Transformer with or without MDR support was evaluated using CDISC blank CRF as the 'gold standard'. Both MDR and user inquiry-supported transformation substantially improved the accuracy of our transformation rules. CDISC Transformer will greatly reduce the workloads and enhance standardized data entry and integration for clinical trial and research in various healthcare domains.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• Journal of Acupuncture Research
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• v.36 no.1
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• pp.1-11
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• 2019

Models of traditional Chinese medicine (TCM) are still difficult to grasp from the view of a Western-cultural background. For proper integration into science and clinical research, it is vital to think "out of the box" of classical sciences. Modern sciences, such as quantum physics, system theory, and information theory offer new models, that reveal TCM as a method to process information. For this purpose, we apply concepts of information theory to propose a "Chinese black box model," that allows for a non-deterministic, bottom-up approach. Considering a patient as an undeterminable complex system, the process of getting information about an individual in Chinese diagnostics is compared to the input-process-output principle of information theory and quantum physics, which is further illustrated by Wheeler's "surprise 20 questions." In TCM, an observer uses a decision-making algorithm to qualify diagnostic information by the binary polarities of "yang" (latin activity) and "yin" (latin structivity) according to the so called "8 principles" (latin 8 guiding criteria). A systematic reconstruction of ancient Chinese terms and concepts illuminates a scattered scientific method, which is specified in a medical context by Latin terminology of the sinologist Porkert [definitions of the Latin terms are presented in Porkert's appendix [1] (cf. Limitations)].

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1796-1800
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• 1991

Picture Archiving and Communication Systems(PACS) provide an integration of digital imaging information in a hospital, which encompasses various imaging equipment, viewing workstations, database archive systems, and a high speed fiber optic network. One of the most important requirements for integration is the standardization of communication protocols to connect devices from different vendors. Since 1985, the ACR-NEMA standard provides a hardware interface, a set of software commands, and a consistent set of data formats for point-to-point interconnection of medical equipment. However, it has been shown to be inadequate for PACS networking environments, because of its point-to-point nature and its inflexibility to allow other services and protocols in the future. Based on previous experience of PACS developments in The University of Arizona, a new communication protocol for PACS networks has been suggested to the ACR-NEMA Working Group VI. The defined PACS protocol is intended to facilitate the development of PACS's capable of interfacing with other hospital information systems. Also, it is intended to allow the creation of diagnostic information data bases which can be interrogated by a variety of distributed devices. A particularly important goal is to support communications in a multivendor environment. The new protocol specifications are defined primarily as a combination of the International Organization for Standardization / Open Systems Interconnection (ISO/OSI) protocols and the data format portion of ACR-NEMA standard. This paper addresses the specification and implementation of the proposed PACS protocol into network node. The protocol specification, which covers Presentation, Session, Transport, and Network layers, is summarized briefly. The implementation has natural extentions to Global PACS environments. The protocol implementation is discussed based on our implementation efforts in the UNIX Operating System Environment. At the same time, results of performance evaluation are presented to demonstrate the implementation of defined protocol. The testing of performance analysis is performed on the PACS prototype node.