• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.92-103
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• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

• Convergence Security Journal
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• v.23 no.1
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• pp.79-87
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• 2023

Precision Medicine, which utilizes personal health information, genetic information, clinical information, etc., is growing as the next-generation medical industry. In Korea, medical institutions and information communication companies have coll aborated to provide cloud-based Precision Medicine Hospital Information Systems (P-HIS) to about 90 primary medical ins titutions over the past five years, and plan to continue promoting and expanding it to primary and secondary medical insti tutions for the next four years. Precision medicine is directly related to human health and life, making information protecti on and healthcare information protection very important. Therefore, this paper analyzes the preliminary research on inform ation protection models that can be utilized in cloud-based Precision Medicine Hospital Information Systems and ultimately proposes research on ways to improve information protection in P-HIS.

• Journal of Oriental Neuropsychiatry
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• v.24 no.1
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• pp.109-122
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• 2013

Objectives : To research the needed Buddhistic ethical beliefs and psychotherapy from representative medical records of oriental medicine. Methods : The baseline data this research used is Myeong-Ui-Lyu-An, Sok-Myeong-Ui-Lyu-An, Ui-Bu-Jeol-Lok and from the variety of medical records; we extracted 22 medical records that refer to Buddhist thoughts. The sequence of medical records is determined by analyzing the contents of all medical records and grouping them by their categories. Results : The representative ethical mind that a doctor needs is the 'mercy thought' from Buddhism. This way, the doctor has 'pity' on patients and expects no reward for what he had done. 'Spells and religious beliefs developed into medical treatment procedures by Buddhism and oriental medicine psychotherapy. Using the belief that everything is made of the mind, which is the point of the 'Hwa-Eum' theory and the realization that the psychotic factors have a big role in the occurrence and progress of sicknesses, we emphasized supportive psychotherapy or more specifically, the suggestive therapy. 'Anguish' is an important point in the occurrence and progress of illnesses. To solve this, we used 'Zen family's 'Zen self-discipline' and ascetic life from Buddhism. According to Buddhism, a human's metal conflict and love or malingering from obsession is the cause of all mind illnesses. To heal these, a doctor must have an insight of the patient's mind more than the symptoms. Conclusions : Buddhistic thoughts suggested clearly the mentality necessary for oriental medical psychotherapist and medical ethics for a doctor.

• Journal of Preventive Medicine and Public Health
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• v.25 no.3 s.39
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• pp.303-311
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• 1992

It is very important to estimate the future medical care expenditure, because medical care expenditure escalation is a big problem not only in the health industry but also in the Korean economy today. This study was designed to project the medical care expenditure in view of population age change. The data of this study were the population projection data based on National Census Data(1990) of the National Statistical Office and the Statistical Reports of the Korea Medical Insurance Corporation. The future medical care expenditure was eatimated by the regression model and the optional simulation model. The significant results are as follows : 1. The future medical care expenditure will be 3,963 billion Won in the year 2000, 4,483 billion Won in 2010, and 4,826 billion Won in 2020, based on the 1990 market price considering only the population age change. 2. The proportion of the total medical care expenditure in the elderly over 65 will be 10.4% in 2000, 13.5% in 2010, and 16.9% in 2020. 3. The future medical care expenditure will be 4,306 billion Won in the year 2000, 5,101 billion Won in 2010, and 5,699 billion Won in 2020 based on the 1990 market price considering the age structure change and the change of the case-cost estimated by the regression model. 4. When we consider the age-structure change and inflation compared with the preceding year, the future medical care expenditurein 2020 will be 21 trillion Won based on a 5% inflation rate, 42 trillion Won based on a 7.5% inflation rate, and 84 trillion Won based on a 10% inflation rate. Consideration of the aged(65 years old and over) will be essential to understand the acute increase of medical care expenditure due to changes in age structure of the population. Therefore, alternative policies and programs for the caring of the aged should be further studied.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.25 no.2
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• pp.143-149
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• 2023

Objective: The purpose of this nationwide age- and sex- matched longitudinal study was to determine the pyogenic spondylitis (PS) increases the incidence of ischemic stroke (IS) in Korea. Methods: From the National Health Insurance Service (NHIS), we collected the patient data for the period from January 1, 2004 to December 31, 2015. PS was classified according to the International Classification of Disease codes M46.2-M46.8, M49.2, and M49.3. By using a 1:5 age- and sex- stratified matching, a total of 628 patients and 3140 control subjects were included in the study. The IS incidence rates in PS and control group was calculated by using the Kaplan-Meier method. The outcome of hazard ratio of IS was estimated by Cox proportional hazards regression analyses. This study did not exclude PS as a result of postoperative complications. Results: According to the study, 51 patients (8.12%) in the PS group and 201 patients (6.4%) in the control group experienced IS. The adjusted hazard ratio of IS in the PS group was 3.419 (95% CI: 2.473-4.729) after adjusting individual medical condition and demographics. Following the results of subgroup analysis, the risk ratio of IS was greater in most of the subgroup categories (male, female, age <65, age >65, non-diabetic, hypertensive, non-hypertensive, dyslipidemic and non-dyslipidemic subgroup). However, the risk of IS did not differ significantly in diabetic subgroup (95% CI: 0.953-4.360). Conclusions: The risk rate of IS increased in patient with pyogenic spondylitis.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.35-40
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• 2020

As we enter the society of the future, efforts to increase healthy living are a major area of concern for modern people. In particular, the development of technology for a healthy life that combines ICT technology with a competitive healthcare industry environment is becoming the next growth engine. Therefore, in this paper, artificial intelligence-based data analysis of the examination results was applied in the health examination process. Through this, a research was conducted to build a knowledge base modeling that can improve the reliability of the overall judgment. To this end, an algorithm was designed through deep learning analysis to calculate and verify the test result index. Then, the modeling that provides comprehensive examination information through judgment knowledge was studied. Through the application of the proposed modeling, it is possible to analyze and utilize big data on national health, so it can be expected to reduce medical expenses and increase health.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of Yeungnam Medical Science
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• v.41 no.2
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• pp.86-95
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• 2024

Background: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on the development of major mental disorders in patients visiting a university hospital. Methods: The study participants were patients with COVID-19 (n=5,006) and those without COVID-19 (n=367,162) registered in the database of Keimyung University Dongsan Hospital and standardized with the Observational Medical Outcomes Partnership Common Data Model. Data on major mental disorders that developed in both groups over the 5-year follow-up period were extracted using the FeederNet computer program. A multivariate Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the incidence of major mental disorders. Results: The incidences of dementia and sleep, anxiety, and depressive disorders were significantly higher in the COVID-19 group than in the control group. The incidence rates per 1,000 patient years in the COVID-19 group vs. the control group were 12.71 vs. 3.76 for dementia, 17.42 vs. 7.91 for sleep disorders, 6.15 vs. 3.41 for anxiety disorders, and 8.30 vs. 5.78 for depressive disorders. There was no significant difference in the incidence of schizophrenia or bipolar disorder between the two groups. COVID-19 infection increased the risk of mental disorders in the following order: dementia (HR, 3.49; 95% CI, 2.45-4.98), sleep disorders (HR, 2.27; 95% CI, 1.76-2.91), anxiety disorders (HR, 1.90; 95% CI, 1.25-2.84), and depressive disorders (HR, 1.54; 95% CI, 1.09-2.15). Conclusion: This study showed that the major mental disorders associated with COVID-19 were dementia and sleep, anxiety, and depressive disorders.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.433-439
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• 2000

Volume rendering is a powerful tool for visualizing sampled scalar values from 3D data without modeling geometric primitives to the data. The volume rendering can describe the surface-detail of a complex object. Owing to this characteristic. volume rendering has been used to visualize medical data. The size of volume data is usually too big to handle in real time. Recently, various volume rendering algorithms have been proposed in order to reduce the rendering time. However, most of the proposed algorithms are not proper for fast rendering of large non-coded volume data. In this paper, we propose a block-based fast volume rendering algorithm using a shear-warp factorization for non-coded volume data. The algorithm performs volume rendering by using the organ segmentation data as well as block-based 3D volume data, and increases the rendering speed for large non-coded volume data. The proposed algorithm is evaluated by rendering 3D X-ray CT body images and MR head images.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.