• 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 Nutrition and Health
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• v.56 no.3
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• pp.330-347
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• 2023

Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

• Journal of The Korea Institute of Healthcare Architecture
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• v.26 no.2
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• pp.71-79
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• 2020

Purpose: The purpose of this study is to derive basic data for desirable location and functions of the integrated care center. Methods: Survey, Questionaire and statistical analysis are the main research method of this study. In order to collect data related to utilization pattern and favorite functions of the senior people, researchers have visited 4 social welfare facilities located in Southern Gyeonggi Province. 403 questionaires have been gathered from 4 facilities and they have been analyzed by using Excel Program of MS. Results: First, compared to other services, healthcare services have been preferred by many older people in Social welfare Facilities. This means that integrated care centers providing healthcare services for older people rather than services for children or disabilities is desirable. Second, Integrated Care Centers had better be established within the walk distance of elderly people. If it is not easy, the introduction of shuttle bus for older people is desirable. Especially, in case of large Care Center. Implications: This study shows that small facility with community care rather than big facility is desirable for small community in the point of friendliness, convenience, economy, etc.. However it is necessary to combine welfare service and healthcare service even in small centers.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Health Policy and Management
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• v.30 no.1
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• pp.15-25
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• 2020

While healthcare data sets include extensive information about patients, many researchers have limitations in analyzing them due to their intrinsic characteristics such as heterogeneity, longitudinal irregularity, and noise. In particular, since the majority of medical history information is recorded in text codes, the use of such information has been limited due to the high dimensionality of explanatory variables. To address this problem, recent studies applied word embedding techniques, originally developed for natural language processing, and derived positive results in terms of dimensional reduction and accuracy of the prediction model. This paper reviews the deep learning-based natural language processing techniques (word embedding) and summarizes research cases that have used those techniques in the health care field. Then we finally propose a research framework for applying deep learning-based natural language process in the analysis of domestic health insurance data.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1137-1144
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• 2020

This paper aims to provide strategic suggestions by analyzing technology trends related to big data, IoT, and artificial intelligence. To this end, analysis was performed using the 2018 national R&D information, and major basic analysis and language network analysis were performed. As a result of the analysis, research and development related to big data, IoT, and artificial intelligence are being conducted by focusing on the basic and development stages, and it was found that universities and SMEs have a high proportion. In addition, as a result of the language network analysis, it is judged that the related fields are mainly research for use in the smart farm and healthcare fields. Based on these research results, first, big data is essential to use artificial intelligence, and personal identification research should be conducted more actively. Second, they argued that full-cycle support is needed for technology commercialization, not simple R&D activities, and the need to expand application fields.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.201-208
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• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1249-1254
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• 2021

Computing technology and networking technology in the era of the 4th industrial revolution are rapidly evolving into an intelligent information society. AR, VR, and MR technologies, which are dual immersive media fields, are being applied in many convergence technologies, especially! The development of the health and healthcare field is actively progressing. In the field of health and healthcare, there are many problems due to aging of the population, increase in chronic progress, lack of infrastructure, and lack of professional manpower. services in the field are adopted. Therefore, this study applies cognitive evaluation through a computing system to the mild cognitive impairment, designs and develops a cognitive judgment platform using augmented reality based on the cognitive judgment technology system design, and integrates AI and BigData-based intelligent cognitive rehabilitation in the future. It is used as basic data for service platform development.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.699-706
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• 2009

The convergence tendency accelerates the realization of the ubiquitous healthcare (u-Healthcare) between the technology including the power generaation and IT-BT-NT of the ubiquitous computing technology. By rapidly analyzing a large amount of collected from the sensor network with processing and delivering to the medical team an u-Healthcare can provide a patient for an inappropriate regardless of the time and place. As to the existing u-Healthcare, since the sensor node all transmitted collected data by using with the Zigbee protocol the processing burden of the base node was big and there was many communication frequency of the sensor node. In this paper, the u-Healthcare system in which it can efficiently apply to mobile apparatuses it provided the transfer rate in which it is superior to the bio-signal delivery where there are the life and direct relation which by using the Bluetooth instead of the Zigbee protocol and in which it is variously used in the ubiquitous environment was designed. Moreover, by applying the EEF(Embedded Event Filtering) technique in which data in which it includes in the event defined in advance selected and it transmits with the base node, the communication frequency and were reduced. We confirmed to be the system in which it is efficient through the simulation result than the existing Electrocardiogram Measurement system.

• Knowledge Management Research
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• v.23 no.4
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• pp.69-89
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• 2022

As we enter a super-aged society, senior citizens' health issues are affecting a variety of fields, including medicine, economics, society, and culture. In this study, we intend to draw implications from unstructured data analysis such as text mining and social network analysis in order to apply digital health care service design for improving the cognitive ability of senior citizens. The research procedure of this study improved the service design methodology into a process suited to the analysis of unstructured data, and six steps were applied. Related keywords that exist on social media, focusing on cognitive improvement and healthcare for senior citizens, were collected and analyzed, and based on these results, the direction of healthcare service design for improving on the cognitive abilities of senior citizens was derived. The results of this study are expected to have academic and practical implications for expanding the scope of the use of big data analysis methods and improving existing healthcare service development methodologies.