• 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.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1290-1304
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• 2020

Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning have been actively studied. However, sufficient medical data are difficult to obtain, and data imbalance between classes hinder the improvement of deep learning performance. To resolve these issues, various studies have been performed, and data augmentation has been found to be a solution. In this review, we introduce data augmentation techniques, including image processing, such as rotation, shift, and intensity variation methods, generative adversarial network-based method, and image property mixing methods. Subsequently, we examine various deep learning studies based on data augmentation techniques. Finally, we discuss the necessity and future directions of data augmentation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.250-261
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• 2020

Today, data subjects should be considered to utilize various personal data. To support this paradigm, the concept of "My Data" has proposed and has realized in various industrial sectors, including medial sectors. Based on the concept of the medical My Data, this paper proposes a personal health record (PHR) and an electronic medical record (EMR) data trading model. Particularly, this paper proposes a system model to support the medical My Data environment and relevant procedure among stakeholders for PHR/EMR data trading that ensures the rights of data subjects. Based on the proposed system model, this paper also proposes various mathematical models to analyze the behavior of stakeholders and shows the feasibility of the proposed data trading model that satisfies the requirements of both data subjects and data consumers.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.89-98
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• 2010

Recently supply of Internet is bringing a important change in medical environments. The hospitals which had a different system is required the system that can efficiently share and exchange medical information. In order to transmission medical information between systems, the Health Level Seven(HL7) interface engine development that can convert medical data to HL7 messages is necessary. The HL7 is a standard protocol for data exchange in healthcare environments. In this paper, I implemented HL7 interface engine for Alzheimer's disease in elderly care facility. The interface engine is composed of the client system and the server system. The client system inputs user's medical care data for the aged, and builds them into HL7 message stream. HL7 messages in the client system transmitted over TCP/IP protocol to the server system. The server system parses and validates this messages stream to the segments and fields and then transmits acknowledgement to the client system. I implemented it using the Java and JavaCC. The study of interface engine implementation can be used meaningfully in electronic health record, telemedicine system, and medical information sharing among various healthcare institutions.

• Proceedings of the CALSEC Conference
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• 2001.02a
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• pp.561-569
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• 2001

□ 인터넷 의료 처방전 시스템 ★ 병원, 약국, 환자를 네트웍화 하여 인터넷으로 병원과 약국간의 실시간 연결, 신뢰성 있는 처방전의 발행하는 등 다양한 서비스를 제공하는 시스템 □ 독창성 ★ 병원 및 약국의 업무 자동화로 정확하고 신속한 처방전 발행을 통한 선진화된 의료 서비스 및 의약 분업의 불편을 최소화하며 의료체계에 대한 데이터 및 프로세스의 기준제시로 전 의료체계 전산화의 비전을 제시(중략)

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.393-402
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• 1999

과거와 다르게 최근의 병원들은 정보화로 인해서 상당한 양의 의료 데이터가 저장되어 있어서 이의 효과적인 이용에 관심을 가지고 있다. 그러나 기존 통합병원정보시스템(Integrated Hospital Information System)은 아직까지 일반관리와 원무관리 중에서 벗어나지 못하고 있다. 품질 좋은 의료 서비스를 제공하기 위해서 환자 중심의 진료 및 진료지원, 임상연구 등을 종합적으로 지원하기 위한 데이터 웨어하우스(Data Warehouse)의 필요성이 대두되기 시작했다. 이에 본 연구는 병원 전체 차원에서 데이터 웨어하우스의 아키텍쳐를 설계하고 개발하는 데 주안점을 두었다. 특히, 임상 데이터 웨어하우스(Clinical Data Warehouse)에 초점을 두었으며 이에 대한 프로토타입은 J 병원에 적용되어서 개발되었다.