• International journal of advanced smart convergence
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• v.5 no.2
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• pp.53-58
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• 2016

With the growing use of big data and data mining, it serves to understand how such techniques can be used to understand various relationships in the healthcare field. This study uses hierarchical methods of data analysis to explore similarities in hospitalization across several New York state counties. The study utilized methods of measuring crowding distance of data for age-specific hospitalization period. Crowding distance is defined as the longest distance, or least similarity, between urban cities. It is expected that the city of Clinton have the greatest distance, while Albany the other cities are closer because they are connected by the shortest distance to each step. Similarities were stronger across hospital stays categorized by age. Hierarchical clustering can be applied to predict the similarity of data across the 10 cities of hospitalization with the measurement of crowding distance. In order to enhance the performance of hierarchical clustering, comparison can be made across congestion distance when crowding distance is applied first through the application of converting text to an attribute vector. Measurements of similarity between two objects are dependent on the measurement method used in clustering but is distinguished from the similarity of the distance; where the smaller the distance value the more similar two things are to one other. By applying this specific technique, it is found that the distance between crowding is reduced consistently in relationship to similarity between the data increases to enhance the performance of the experiments through the application of special techniques. Furthermore, through the similarity by city hospitalization period, when the construction of hospital wards in cities, by referring to results of experiments, or predict possible will land to the extent of the size of the hospital facilities hospital stay is expected to be useful in efficiently managing the patient in a similar area.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1207-1212
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• 2018

The ultrasound image uses ultrasonic pulses to receive the reflected waves and construct an image necessary for diagnosis. At this time, when the signal becomes weak, noise is generated and a slight difference in brightness occurs. In addition, fluctuation of image due to breathing phenomenon, which is the characteristic of ultrasound image, and change of motion in real time occurs. Such a noise is difficult to recognize and diagnose visually in the analysis process. In this paper, morphological features are automatically extracted by using image processing technique on ultrasound acquired images. In this paper, we implemented a GPU - based fast filter using a cloud big data processing platform for image processing. In applying the GPU - based high - performance filter, the algorithm was run with performance 4.7 times faster than CPU - based and the PSNR was 37.2dB, which is very similar to the original.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.552-555
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• 2017

It is de-identification that emerged to find the trade-off between the use of big data and the protection of personal information. In particular, in the field of medical that deals with various semi-identifier information and sensitive information, de-identification must be performed in order to use medical consultation such as EMR and voice, KakaoTalk, and SNS. However, there is no separate law for medical information protection and legislation for de-identification. Therefore, in this study, we present the current status of de-identification of personal information, the status and case of de-identification of medical information, and finally we provide issues and solutions for medial information protection and de-identification.

• Korea Journal of Hospital Management
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• v.26 no.3
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• pp.56-67
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• 2021

There are few domestic studies on medical services in medically vulnerable areas where medical use is not met due to a lack of medical resources. The past studies on smart medicine targeting medically vulnerable areas grasp only the overall satisfaction level, or the sub-dimensions of satisfaction are not classified clearly. Also, it lacks consideration of the patient's needs. This study aims to analyze the effect of users' experience of the smart medicine pilot project conducted in medically vulnerable areas on satisfaction and demand. The user's experience was measured by variables in the dimensions of structure, process, and outcome. Among the pilot project participants, 282 subjects responded to the 2019 survey. Using the hierarchical regression method, we tried to find out the determinants of satisfaction and service demands. Experience factors affecting satisfaction were found to be accessibility, certainty, effectiveness, and efficiency. In addition, it was found that the demand in their 60s was high and that accessibility, certainty, effectiveness, and efficiency had a statistically significant effect on the demand. It is expected that the smart medicine pilot project will be effectively operated by well utilizing the factors influencing satisfaction and demand revealed in this study.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4613-4617
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• 2012

National cancer incidence data were utilized to analyze trends in esophageal cancer incidence in China in order to provide basic information for making cancer control strategy. We retrieved and re-sorted valid esophageal cancer incidence data from National Central Cancer Registry Database over 20 years period from 1989 to 2008. Crude incidence and age-standardized incidence rates were calculated for analysis, with annual percent change estimated by Joinpoint software for long term trend analysis. The crude incidence rate of esophageal cancer was found to have remained relatively stable in both urban and rural areas over the 20 year period. Age standardized incidence rate (ASR) in cancer registration areas decreased from 39.5/100,000 in 1989 to 23.0/100,000 in 2008 in all areas (AAPC=-3.3%, 95% CI:-2.8~-3.7). The trend was no change in urban areas and 2.1% average annual decrease observed in rural aras. Before the year of 2000, esophageal cancer incidence rates significant decreased with 2.8% annually and then the rates kept stable. Over 20 years from 1989 to 2008, esophageal cancer age standardized incidence rate in cancer registration areas decreased with time. However, esophageal cancer is still a big issue and efforts for control should be continuously enhanced. Cancer registration is playing an important role in cancer control with the number of registries increasing and data quality improving in China.

• Journal of Chest Surgery
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• v.47 no.5
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• pp.437-443
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• 2014

The JCVSD (Japan Cardiovascular Surgery Database) was organized in 2000 to improve the quality of cardiovascular surgery in Japan. Web-based data harvesting on adult cardiac surgery was started (Japan Adult Cardiovascular Surgery Database, JACVSD) in 2001, and on congenital heart surgery (Japan Congenital Cardiovascular Surgery Database, JCCVSD) in 2008. Both databases grew to become national databases by the end of 2013. This was influenced by the success of the Society for Thoracic Surgeons' National Database, which contains comparable input items. In 2011, the Japanese Board of Cardiovascular Surgery announced that the JACVSD and JCCVSD data are to be used for board certification, which improved the quality of the first paperless and web-based board certification review undertaken in 2013. These changes led to a further step. In 2011, the National Clinical Database (NCD) was organized to investigate the feasibility of clinical databases in other medical fields, especially surgery. In the NCD, the board certification system of the Japan Surgical Society, the basic association of surgery was set as the first level in the hierarchy of specialties, and nine associations and six board certification systems were set at the second level as subspecialties. The NCD grew rapidly, and now covers 95% of total surgical procedures. The participating associations will release or have released risk models, and studies that use 'big data' from these databases have been published. The national databases have contributed to evidence-based medicine, to the accountability of medical professionals, and to quality assessment and quality improvement of surgery in Japan.

• The Korean Journal of Pain
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• v.36 no.4
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• pp.458-464
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• 2023

Background: Fibromyalgia (FM) is a complex disorder characterized by widespread chronic pain and tenderness in the muscles, ligaments, and soft tissues. It is a chronic pain condition often accompanied by other symptoms and comorbidities. To effectively manage FM, it is crucial to obtain fundamental epidemiological data pertaining to the target population. Therefore, this study was conducted to elucidate the epidemiological characteristics of FM in the Korean population. Methods: Population-based medical data of 51,276,314 subscribers to the National Health Insurance Service of Korea from 2014 to 2018 were used in this study. Results: The overall incidence of FM ranged from 441 (2014) to 541 (2018) cases per 100,000 person-years, with a higher prevalence observed among female patients compared to male patients. The incidence gradually increased until middle age, followed by a decrease. The highest incidence rates were observed in the fifth decade of life for females and the sixth decade of life for males. When categorizing the affected parts of the body, the shoulder region was observed to be the most frequently affected. A comparison of the drug prescriptions based on medical specialty showed that antidepressants were the most commonly prescribed medications. The management of FM leads to consistent increases in medical expenses, regional disparities, and variations in prescription patterns across different medical specialties. Conclusions: The findings of this study will not only contribute to the understanding of FM characteristics but also provide a vital foundation for efficient management of FM in Korea.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.870-883
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• 1997

The health center has to play an important role in promoting community health and satisfying a variety of community health needs and demands in the decentralized Korea. The nearly enacted Community Health Act compels every health center to make its own health plans which intend to deal with local health problems and plan its future health care. This obligation is obviously a big burden to most health centers. They do not have experiences in and abilities of making local health care plans. In order to establish a systematic community health plan, health centers have to concentrate their efforts on enhancing the ability of making health care plan through gathering and analysing the local health informations. However, it is very difficult in reality. This is simply because it will take long time to accomplish these activities. It seems natural that various professionals and researchers participate in carrying out the process of making community health plan in the initial stage. No standardized methodology and analysing framework exist even in the health professional society. Nonetheless, it is common to introduce survey research methodologies in analysing consumer's health care utilization and cost, and in identifying factors influencing health behaviors. Many researchers and professionals have applied social survey methodologies in obtaining information on providers and health policy makers as well. The authors have found that few studies have ever utilized local health data stored at the self-employed medical insurance society as the data source of planning activities. The purpose of this study is to illustrate the usefulness of the data stored at the Sung-Dong Gu Self-employed Medical Insurance Society in establishing the community health plan. The major contents of this study are as follows ; 1. frequency of utilization by age, area, sex, type of medical care institutions, and some major diseases 2. Medical treatment by type of medical care institutions, by classification of 21 diseases, by frequency of three-character categories 3. Medical treatment of major neoplasm and some chronic diseases by age, sex, and area. The conclusion of this study is that it is of great potentiality to find out the local health problems and to use them in blueprinting the community health plan through comparing the frequency of medical utilization analyzed by a variety of variables with NHI health data or the health data from survey research.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

• The Korean Journal of Health Service Management
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• v.11 no.1
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• pp.117-130
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• 2017

Objectives : In this study, 3,107 patients were used to evaluate the impact based on raw data of 2014 and the health status and medical expenses income quintile was collected and data was analyzed. Methods : Analysis method was the average comparison, ANOVA, subjected to a multiple logistic regression analysis, the statistical test was the t-test and the scheffe post verification. Results : Gender(p<.000), age(p<.000), marital status(p<.000) educational status (p<.000), easement(p<.000), medication(p<.000), subjective health status(p<.005) were analyzed. First quintile identified that the highest amount was spent in the Chungcheong region, the 2nd quintile showed that the highest output was in the Gyeongsang region. The 3rd and 4th quintiles indicated that the highest expenditure was in the Seoul metropolitan region. The 5th quintile showed that the Chungcheong was the highest once again and the Jeolla region was the lowest in terms of expediture. Conclusions : Future medical research on income will require the government's Big Data collection to create the primary basis for policy making in order to improve the efficiency, effectiveness and equity of medicine spending.