• 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.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.341-347
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• 2014

Among methods of the big data process, big data process under the cloud environment is becoming a main topic. As part of solving faced problem and strengthening industrial competitiveness in the medical and health industry, discussion on ways to activate big data is actively being conducted. Because the reason is a paradigm shift, saving pressure for increasing health care costs, and increased consumer interest for the level of service. In this paper, we find out the relationship between the cloud and big data. And we are to research and analysis a cloud-based big data case in the medical field. Finally we propose the efficient utilization and future outlook. For the smooth functioning of cloud-based medical big data, we have to solve the problems like infrastructure extension, analysis/application software development, and professional manpower training. In addition, we have to correct insufficient laws maintenance to the Cloud utilization, and improve the security and the recognition to personal information, and solve authority for data centralization.

• Information and Communications Magazine
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• v.32 no.11
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• pp.3-7
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• 2015

최근 활발히 연구되고 있는 빅데이터와 의료 영역이 융합되면서, 보건의료서비스 분야에서는 데이터 집약적이고 공간을 초월한 새로운 서비스패러다임의 움직임이 진행되고 있다. 본고에서는 이러한 빅데이터를 활용하여 건강증진 및 예방을 위하여 생활 속에서 제공되고 있는 생활환경 및 보건 데이터 기반의 라이프케어 서비스동향과 기술에 관하여 알아본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.565-566
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• 2024

본 연구는 서울시의 우울증 진단 경험률 통계 데이터를 분석하여 지역별 우울증 발생 패턴과 인구 통계적 특성을 파악하고자 하였습니다. 결측치 처리와 이상치 조정을 통한 데이터 전처리 후, 연령대와 성별, 교육 수준 등 다양한 변수에 따른 우울증 진단률의 분포를 탐색적 데이터 분석을 통해 시각화하였습니다. 특히 여성과 고령 인구에서 높은 우울증 경험률을 관찰하였으며, 이를 통해 맞춤형 정신건강 개선 방안을 제시하고자 하였습니다. 본 연구는 정책 입안자와 보건 전문가들에게 유용한 인사이트를 제공하고, 효과적인 우울증 관리 및 예방 전략 개발에 조금이라도 기여할 것으로 기대됩니다.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• 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.

• The Korean Society of Law and Medicine
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• v.22 no.3
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• pp.31-55
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• 2021

The Personal Information Protection Act, one of the revised 3 Data Laws, established a special cases concerning pseudonymous data. As a result, a personal information controller may process pseudonymized information without the consent of data subjects for statistical purposes, scientific research purposes, and archiving purposes in the public interest, etc. In addition, as a follow-up to the revised Personal Information Protection Act, a 'Guidelines for Utilization of Healthcare Data' was prepared, which deals with the pseudonymization in the medical sector. The guidelines are meaningful in that they provide practical criteria for accomplices by defining specific interpretations and examples that take into account the characteristics of healthcare data. However, the guidelines need to clarify the purpose of using pseudonymous data and strengthen the fairness of the composition of the data deliberation committee. The guidelines also require establishing a healthcare data compensation framework and strengthening the protection of rights for vulnerable subjects. In addition, the guidelines need to be adjusted for inconsistency with the Bioethics and Safety Act and the Medical Service Act. It is expected that this study will contribute to the creation of a safe environment for the utilization of healthcare data as well as the improvement of related laws and systems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.101-109
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• 2014

Medical technology development and increase the income level of a "Long and healthy Life=Wellness," with the growing interest in actively promoting and maintaining health and wellness has become enlarged. In addition, the demand for personalized health care services is growing and extensive medical moves of big data, disease prevention, too. In this paper, the main interest in the market, highlighting wellness in order to support big data-driven healthcare quality through patient-centered medical services purposes. Patients with drug dependence treatment is not to diet but to improve disease prevention and treatment based on analysis of big data. Analysing your Tweets-daily information and wellness disease prevention and treatment, based on the purpose of the dictionary. Efficient big data analysis for node while increasing processing time experiment. Test result case of total access time efficient 26% of one node to three nodes and case of data storage is 63%, case of data aggregate is 18% efficient of one node to three nodes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.367-370
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• 2022

본 연구는 코로나19 발생 전·후 공공의료를 둘러싼 사회적 인식변화를 뉴스빅데이터를 통해 파악하고자 시도되었다. 뉴스빅데이터는 코로나19 확진자가 처음 발생한 2020년 1월을 기준으로 나누었으며, 코로나19 발생 이전(2018년 1월~2019년 12월, 총 24개월) 40,834건과 코로나19가 발병 이후(2020년 1월~2021년 12월, 총 21개월) 61,761건이었다. 수집된 빅데이터는 R 4.1.1 for Windows를 활용하여 단어 빈도 분석, 연관규칙분석을 실시하였다. 연구결과, 코로나19 발생 전후 뉴스기사에서 공공의료를 둘러싼 핵심어를 비교할 때 코로나19 발생 후에 발생 전보다 큰 폭으로 상승한 단어는 '확산'(664%), '대응'(658%), '의사'(518%), '상황'(504%), '공공병원'(486%), '의료진'(455%), '확충'(324%), '인력'(305%), '어려움'(272%), '정부'(247%)순으로 나타났다. 코로나19 발생 전후 공공의료를 둘러싼 키워드의 연관규칙 분석을 통해서 의료의 패러다임이 일자리 산업에서 감염증 대응을 위한 보건의료로 전환되는 것을 알수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.161-163
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• 2018

환자의 의료데이터를 각 의료기관에서 통합적으로 저장, 연람 시에 메타데이터를 사용, 리소스 간의 연관관계를 이용한 온톨로지 방식을 사용하여 의료정보를 체계적으로 접근, 열람 할 수 있게 한다. 데이터들을 저장 연람 시에 환자의 생체정보를 사용해야만 저장, 연람할 수 있게 만들어 정보를 환자의 인지 하에서만 접근할 수 있도록 한다. 또한, 생체 인증 시 생성되는 공개키로 블록을 생성, 저장하는 기법을 사용해 외부의 접근으로부터 보호 할 수 있도록 하였다.