• Management & Information Systems Review
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• v.39 no.3
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• pp.17-35
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• 2020

The purpose of this study is to develop business models for current situational scenarios reflecting customer needs emphasize the need for implementing a logistics cooperation system by analyzing big data to strengthen SCM competitiveness capacities. For healthcare SCM competitiveness needed for the logistics cooperation usage intent, they were divided into product quality, price leadership, hand-over speed, and process flexibility for examination. The wordcloud results that analyzed major considerations to realize work efficiency between medical institutes, words like unexpected situations, information sharing, delivery, real-time, delivery, convenience, etc. were mentioned frequently. It can be analyzed as expressing the need to construct a system that can immediately respond to emergency situations on the weekends. Furthermore, in addition to pursuing communication and convenience, the importance of real-time information sharing that can share to the efficiency of inventory management were evident. Accordingly, it is judged that it is necessary to aim for a business model that can enhance visibility of the logistics pipeline in real-time using big data analysis on site. By analyzing the effects of the adaptability of a supply chain network for healthcare SCM competitiveness, it was revealed that obtaining competitive capacities is possible through the implementation of logistics cooperation. Stronger partnerships such as logistics cooperation will lead to SCM competitive capacities. It will be necessary to strengthen SCM competitiveness by searching for a strategic approach among companies in a direction that can promote mutual partnerships among companies using the joint logistics system of medical institutes. In particular, it will be necessary to search for ways to utilize HCSM through big data analysis according to the construction of a logistics cooperation system.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.703-705
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• 2023

본 연구는 대화 중 생체신호 데이터를 활용하여 감정 인식 분야에서 더욱 정확하고 범용성이 높은 인식 기술을 제안한다. 이를 위해, 먼저 대화별 길이에 따른 측정값의 개수를 동일하게 조정하고 효과적인 생체신호 데이터의 조합을 비교 및 분석하기 위해 차원 축소 기법인 T-SNE (T-distributed Stochastic Neighbor Embedding)을 활용하여 감정 라벨의 분포를 확인한다. 또한, AutoML (Automated Machine Learning)을 이용하여 축소된 데이터로 감정을 분류 및 각성도와 긍정도를 예측하여 감정을 가장 잘 인식하는 생체신호 데이터의 조합을 발견한다.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.16-20
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• 2018

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.515-526
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• 2020

Digital healthcare is a combined area of medical field and IT and various information on digital healthcare is provided in social media. This study aims to find the research trend of digital healthcare by collecting and analyzing data related to digital healthcare through the social media. The data were collected from Naver and Daum's news and blogs from January 2008 to June 2019. Major keywords with high frequency were extracted and visualized with wordcloud and network analysis was used to analyze the relationship between major keywords. Research combining medical field and IT from 2008 to 2001, various convergence research based on medical field and IT from 2012 to 2015, convergence research that applied the 4th industrial revolution technologies such as big data, blockchain and AI were actively conducted from 2016 to June 2019.

• Clinical and Experimental Pediatrics
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• v.64 no.4
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• pp.141-148
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• 2021

There are very scant data on the epidemiology of primary immunodeficiency diseases (PIDs) in Korea. Here we attempted to estimate the PID epidemiology and disease burden in Korea. A systematic review was performed of studies retrieved from the PubMed, KoreaMed, and Google Scholar databases. Studies on PIDs published in Korean or English between January 2001 and November 2018 were analyzed. The number of PID patients and the healthcare costs were estimated from Health Insurance Review and Assessment Service (HIRA) Korea data for 2017. A total of 398 PID patients were identified from 101 reports. Immunodeficiencies affecting cellular and humoral immunity were reported in 11 patients, combined immunodeficiency with associated or syndromic features in 40, predominantly antibody deficiencies in 144, diseases of immune dysregulation in 58, congenital defects of phagocytes in 104, defects in the intrinsic and innate immunity in 1, auto-inflammatory disorders in 4, complement deficiencies in 36, and phenocopies of PID in none. From the HIRA reimbursement data, a total of 1,162 outpatients and 306 inpatients were treated for 8,166 and 6,149 days, respectively. In addition, reimbursement was requested for 8,200 outpatient and 1,090 inpatient cases and $1,924,000 and $4,715,000 were reimbursed in 2017, respectively. This study systematically reviewed published studies on PID and analyzed the national open data system of the HIRA to estimate the disease burden of PID, for the first time in Korea.

• The Korean Journal of Pain
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• v.30 no.2
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• pp.142-150
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• 2017

Background: There have been few studies about pain using a big data. The purpose of this study was to identify the prevalence of pain, and trends of pain associated with chronic diseases and personal out-of-pocket medical expenditures over time. Methods: Subjects were 58,151 individuals, using the Korea Health Panel from 2009 to 2013. Chi-square and multinomial logistic regression were conducted to identify the prevalence and odds ratios (ORs) of pain. Repeated measures ANOVA was used to find the trend over these 5 years. Results: Prevalence of mild and severe pain was 28.1% and 1.7% respectively. The ORs of mild and severe pain were 1.6 and 1.4 in females compared with males. From 2009 to 2013, numbers of chronic diseases producing mild pain were 2.1, 2.4, 2.8, 2.9, and 3.1 and those producing severe pain were 3.0, 3.4, 3.9, 4.2, and 4.4, respectively. After applying the average South Korean inflation rate by year over 5 years, the annual, personal out-of-pocket medical expenditures (unit: ￦1,000) for mild pain were 322, 349, 379, 420, and 461, and those for severe pain were 331, 399, 504, 546, and 569, respectively (P < 0.0001). Conclusions: The pain prevalence was 29.8%. The numbers of chronic diseases and the personal out-of-pocket medical expenditures revealed increasing trends annually, especially in those with pain. Therefore, to eliminate and alleviate the pain, there needs to be further study for developing a systemic approach.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.37-44
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• 2021

At the time of entering the super-aged society, the health problem of the elderly is becoming more prominent due to the rapid digital era caused by COVID-19, but the gap between welfare budgets and welfare benefits according to regional characteristics is still not narrowed and there is a significant difference in emergency medical access. In response, this study proposes an ICT-based New Normal Smart Care System (NNSCS) to bridge the gap I n health and medical problems. This is an integrated system model that links the elderly themselves to health care, self-diagnosis, disease prediction and prevention, and emergency medical services. The purpose is to apply location-based technology and motion recognition technology under smartphones and smartwatches (wearable) environments to detect health care and risks, predict and diagnose diseases using health and medical big data, and minimize treatment latency. Through the New Normal Smart Care System (NNSCS), which links health care, prevention, and rapid emergency treatment with easy and simple access to health care for the elderly, it aims to minimize health gaps and solve health problems for the elderly.

• 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.