• Journal of Korean Medical Science
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• v.33 no.53
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• pp.343.1-343.8
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• 2018

Background: Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases. Methods: Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA. Results: Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable. Conclusion: We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.189-195
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• 2019

This study is to investigate the effects of demographic characteristics and lifestyles on obesity in the community. The nurse measured the obesity level of 143 residents living in one apartment in Seoul, Korea. Demographics and lifestyles were analyzed by frequency and percentage and obesity level was analyzed by logistic regression analysis. In particular, as the age increases, the probability of obesity increases by 1.109 times. These results show that the development and application of community health promotion programs for obesity are important. Through this study, we discovered that obesity is significantly related to the demographic characteristics and lifestyles of the individual. And also, it is expected that these findings will be used as the basic information to plan a tailored obesity program taking into account local community environment and personal characteristics.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.113-121
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• 2020

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1321-1322
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• 2012

With the advance of Information Technology (IT) and dynamic requirements, diverse application services have been provided for end users. With huge volume of these services and information, users are required to acquire customized services that provide personalized information and decision at particular extent of time. The case is more appealing in healthcare, where patients wish to have access to their medical record where they have control and provided with recommendation on the medical information. PHR (Personal Health Record) is most prevailing initiative that gives secure access on patient record at anytime and anywhere. PHR should also incorporate decision support to help patients in self-management of their diseases. Available PHR system incorporates basic recommendations based on patient routine data. We have proposed decision support service called "Smart CDSS" that provides recommendations on PHR data for diabetic patients. Smart CDSS follows HL7 vMR (Virtual Medical Record) to help in integration with diverse application including PHR. PHR shares patient data with Smart CDSS through standard interfaces that pass through Adaptability Engine (AE). AE transforms the PHR CCR/CCD (Continuity of Care Record/Document) into standard HL7 vMR format. Smart CDSS produces recommendation on PHR datasets based on diabetic knowledge base represented in shareable HL7 Arden Syntax format. The Smart CDSS service is deployed on public cloud over MS Azure environment and PHR is maintaining on private cloud. The system has been evaluated for recommendation for 100 diabetic patients from Saint's Mary Hospital. The recommendations were compared with physicians' guidelines which complement the self-management of the patient.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.67-73
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• 2019

The blood pressure measurement is calculated as a value corresponding to the pressure of the blood vessel using the pressure from the outside for a long time. Due to the recent miniaturization of measurement equipment and the ICT combination of personal healthcare systems, a system that enables continuous and real-time measurement of blood pressure with a sensor is required. In this study, blood pressure was measured using pulse transit time using Photoplethysmography. In this study, blood pressure was estimated by using systolic blood pressure. And it is possible to make measurement only with PPG itself, which can contribute to making a micro blood pressure measuring device. As a result, systolic blood pressure and PPG's S1-P and P-S2 were used to analyze the possibility of blood pressure estimation.

• Journal of IKEEE
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• v.23 no.1
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• pp.74-79
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• 2019

As the desire for healthcare increases due to the ageing population and the well-being boom, interest in fitness is increasing. People go to the fitness center to exercise, but many people do not know how to exercise, so they often make the order of exercise and the exercise posture wrong. This not only reduces the effectiveness of the exercise, but also increases the risk of injury. This paper describes the development process of fitness system using smart mirror. This system allows people to follow the motion by themselves in front of the smart mirror, and if the exercise posture goes wrong, in real time it checks and corrects for the wrong point. By using this system, people will be able to exercise by themselves in the right posture without the help of a trainer. Also, as a result, people will be able to exercise at any time they want without the constraint of time commitment with a trainer and the cost will be reduced.

• Journal of Society of Preventive Korean Medicine
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• v.23 no.1
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• pp.61-72
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• 2019

Introduction : The aim of this study was to investigate menstrual pattern according to Mibyeong Index(MBI) and compare Mibyeong symptom score according to severity of menstrual pain. Mibyeong Index was designed to measure inconvenience and resilience of subject's complains including four physical symptoms(fatigue, pain, low sleep quality, indigestion) and mental distress including anxiety, anger, depression. Method : We used the clinical data of Korean medicine Date Center(KDC) for subjects who participated in the study of 'Clinical research for collecting of clinical cases based on the personal type of Mibyeng' conducted in Seoul, from June 26, 2015 to June 26, 2017. A total of 566 fertile women aged 30 to 50 who completed the questionnaires were included in this study. In this study, we used items of Mibyeong Index and the menstrual pattern by self-report questionnaires. The date were analyzed through Kruskal Wallis test, Pearson's chi-square test, and one-way ANOVA using SPSS statistics 19.0. Results : Mibyeong status was significantly associated with severity of menstrual pain(p<0.001), worst day of menstrual pain(p<0.05), and type of menstrual pain(low back pain, nausea, depression, and none (respectively, p<0.05)). In addition, The 7-subtype score (fatigue (p<0.001), pain(p<0.001), low sleep quality(p<0.001), indigestion(p<0.001), anxiety(p<0.05), anger(p<0.05) and depression(p<0.05)) of Mibyeong index and total score of Mibyeong Index (p<0.001) showed significant difference for severity of menstrual pain. Conclusion : This result indicates that a close relationship between Mibyeong status and severity of menstrual pain. Further studies are needed but, we hope that this results will be used as the basic data to improve mibyeong status through health care to alleviate dysmenorrhea.

• Health Policy and Management
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• v.31 no.3
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• pp.244-254
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• 2021

The management of emerging infectious diseases cannot help but completely depend on non-pharmaceutical interventions in the early stages of the outbreak. Consequently, South Korea has developed and implemented the 3T (test-trace-treat) models, non-pharmaceutical infection prevention and control (IPC) measures, in response to the coronavirus disease 2019 (COVID-19) pandemic. The IPC measures have gained global attention, rendering them to be essential in the development of a shareable, reusable, and applicable protocol for future pandemics. This study was conducted to identify the requirements necessary for standardizing the IPC measures. Three new work items of the 18 3T models were proposed to ISO/TC 304 (International Organization for Standardization/Technical Committee 304; healthcare organization management). Requirements for each IPC measure, identified by participating members (P-members) countries during the ISO ballots, were analyzed in this study. The three new work items were approved by the P-members countries after a 3-month ballot. There was a consensus that the three IPC measure models should be International Standards (IS). Other comments include (1) the models should include not only COVID-19 but also any respiratory pandemic; and (2) keep donning of level D protection at screening sites as an optional protocol, in consideration for the lack of personal protective equipment. Standardization is a systematic process of developing internationally agreed-upon wisdom and knowledge that consider and respect the diversity and universality of each country. It is expected that such standardized applicable IPC measure models contribute to global efforts to rapidly respond to a public health emergency of international concern during its early stages.