• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1032-1041
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• 2005

Now a days, PACS and the other image sharing systems use only high-level metadata for hospital to retrieve images. So if you want to retrieve some images, you have to know exact information about the patient. In this paper, we developed a Image Retrieval System based on MPEG-7 to retrieve medical images more efficiently. This system offers keyword retrieval using high-level metadata based of DICOM and similarity retrieval using low-level metadata based on MFEG-7. And we integrated high-level metadata and low-level metadata to retrieve medical images more exactly.

• Annual Conference of KIPS
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• 2009.04a
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• pp.1274-1276
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• 2009

병원에서 사용되는 진단검사는 고가의 분석기를 사용하거나 육안으로 비교표와 확인해야한다. 또한 환자의 진단 기록과 근거가 유지되지 않는 문제가 있다. 이런 문제를 해결하고자 디지털 데이터화된 의료정보를 바탕으로 통합적인 진단검사 처리 환경을 제공하는 의료 정보처리 시스템을 설계 및 구현하였다.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.769-778
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• 2002

The LEX system is a XML-based framework for medical information consolidation. The Lex makes it possible for heterogeneous HISs(Hospital Information Systems) exchange and share HL7 messages by storing the messages into a single Central Clinical Database. In this paper, we propose a HL7 message server independently interoperable from existing HIS to generate HL7 messages, and design an XML database schema suitable for storing and manipulating such data. We also propose a new DTD for efficient transformation of HL7 messages to XML documents for storage saving as well as supporting patient-oriented information retrieval.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.128-136
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• 2014

Recently, although exponentially growing the quantity of information that have been used and shared on internet networks, the patient information of each medical center have not been used and shared among medical centers due to the protection of patients privacy and the different database schema. To address this problem, we have studied the data structure of the patient information, the standard of medical information for patients we propose a patient information sharing system design that each medical center is able to use and share the patient information among medical centers in spite of different patient information systems with protecting patients privacy.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.177-193
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• 2018

The business process management system that can integrate and manage a medical institution's processes has been increased importance to adapt to constantly changing medical environments and share information with various external medical institutions. The business process management system is an automated process tool that converts the graphic-based BPMN process model into a WS-BPEL, the execution language of the web service environment, and executes it through the process engine. However, the BPMN process model can be difficult to convert into WS-BPEL due to several ambiguities and structural inconsistencies. The process model may also contain structural anomalies that can lead to execution errors during process execution. In this paper, we present business process model formalization and structural anomaly verification techniques for facilitating integrated process management in medical institutions. Through the case study based on the IHE profile, we presented a formalized BPMN process model and verify the structural anomalies. We show the superiority of the proposed technique through comparative experiments with other related works.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.142-144
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• 2002

대부분의 내용기반 이미지 검색 시스템은 이미지의 특징 벡터인 색상, 모양, 그리고 질감에 의해서 유사한 이미지를 검색하는 기법을 제공하고 있다. 최근 이러한 내용기반 이미지 검색 기술은 의료 영상 이미지와 같은 다양한 분야에 적용되고 있으며, 이에 따라서 의료 이미지를 분석하여 저장, 검색하기 위한 데이터베이스 시스템이 증가하고 있다. 그러나, 대량의 이미지로부터 원하는 이미지를 검색하기 위해서는 이미지의 메타데이타를 효율적으로 표현해야 하며, 의미성과 이미지의 특징 데이터를 통합적으로 저장 관리 할 수 있는 이미지 데이터베이스를 설계하고 구축해야만 한다. 본 논문에서는 기존의 내용기반 이미지 검색 기법을 살펴보고. 이미지를 내용기반으로 분류하고 저장할 수 있는 데이터베이스 시스템을 설계하여 효율적인 의미기반 검색을 지원말 수 있는 모델을 제시한다. 다계층 메타데이타 레이어 구조로 이미지에 대한 개념 지식 모델을 표현하고, 이미지내의 객체를 메타데이타로 표현하여 분류할 수 있는 모델을 제안한다. 또한, 이미지 내용검색을 지원하기 위한 시스템 구조를 설계하고, 메타데이타가 저장되기 위한 관계형 모델을 스타 스키마의 형태로 제시한다. 제안된 방법은 의미적인 이미지 내용 검색 방법의 지원에 활용될 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.205-212
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• 2016

Recently, researches for the integration and standardization of clinical data in the Western medicine and Korean medicine is in progress. If an integration of similar clinical data as well as heterogeneous clinical data is possible based on one standardization, we can able to derive implicit medical knowledge from integrated clinical data. In this paper, we implemented Korean clinical database based on internationally known CDISC standardization to efficiently store Korean clinical data and constructed E-CRF system for convenient data input in clinical sites. Furthermore, we showed example of an integration of Korean clinical data from 4 clinical sites. The results of our study will help to establish the foundation for the extraction of implicit medical knowledge from integrated clinical data. Also, our results may support efficient management through data integration, prevention of repetitive or unnecessary clinical trials, facilitation of collaborative study and convenient research through the distribution of refined clinical data.

• Annual Conference of KIPS
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• 2002.04a
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• pp.113-116
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• 2002

PACS는 의학용 영상 정보의 저장, 판독 및 검색 기능 등의 수행을 통합적으로 처리하는 시스템을 말한다. 그 중 방사선 검사 곁과를 디지털 이미지로 변환하여 대용량 기억장치에 저장시키는데 기에 따른 저장장치의 비용증가와 데이터의 효율적인 관리가 필요하게 되었다. 따라서 의료 영상 데이터의 효율적인 이동을 위한 이미지 저장 시스템의 모듈을 설계하였다.

• Journal of Service Research and Studies
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• v.2 no.2
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• pp.45-51
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• 2012

U-healthcare is real-time monitoring of personal biometric information using by portable devices, home network and information and communication technology based healthcare systems, and fused together automatically to overcome the constraints of time and space are connected with hospitals and doctors. As u-healthcare gives health service in anytime and anywhere, it becomes to be a new type of medical services in patients management and disease prevention. In this paper, recent changes in prevention-oriented care is analyzed in becoming early response for Healthcare Information System by requirements analysis for technology development trend. According to the healthcare system, PACS, OCS, EMR and emergency medical system, U-healthcare is presenting the design of a patient-centered integrated client system. As the relationship between the meaning of the terms is used in the ontology, information models in the system is providing a common vocabulary with various levels of formality. In this paper, we propose an ontology-based system for patient-centered services, including the concept of clustering to clustering the data to define the relationship between these ontologies for more systematic data.