• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1351-1356
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• 2019

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic patients based on the Big Data Platform. As a method of study, second data of 2017 community health survey and Statistics Korea by City·Gun·Gu public office were used and a multi-level analysis was conducted after separating EQ-5D index, individual factor and community factor. As a result, men, age, education level, monthly household income, having economic activity, the number of sports infrastructure were positively associated with the quality of life, and subjective health not good, extremely perceived stress were negatively associated with the quality of life. Research will continue to provide a platform independent of hardware that can utilize the cloud and open source for medical big data analysis in the future.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.73-86
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• 2023

The purpose of this study is to prepare basic data that can be applied to the development of personalized programs in which chronic patients can actively participate in health care on their own, by analyzing the relationship between health literacy, level of metal health, and level of life health of patients with chronic diseases. For the study, the Korean Medical Panel's annual data(Version 2.1) was used, and 4,095 people aged 19 or older with chronic diseases and without disabilities were extracted, and frequency analysis, t-test, ANOVA, and chi-squared goodness of fit test, etc. were performed with IBM SPSS Statistics 26.0. As a result, it was found that the higher health literacy, the higher level of mental health and level of life health. In addition, the distribution between health literacy, level of mental health, and level of life health was found to be different from each other. Respondents with higher ability to health literacy tend to evaluate level of metal health and life health lower, and the rate of change in this trend was relatively higher than the rate of change in the tendency to evaluate level of mental health and life health higher in respondents with lower ability to health literacy.

• Korea Journal of Hospital Management
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• v.13 no.4
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• pp.1-26
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• 2008

This study examines the statistical relationship between medical specialists and managerial performance, using regression analysis with the number of medical specialists per 100 beds as the independent variable and the managerial performance index as the dependent variable. Managerial performance index incorporated the number of out-patients per specialist, the number of in-patients per specialist, the volume of revenue per specialist, the number of beds per specialist, and the average length of stay. To compare different groups of hospitals, dummy variable was applied to five groups of hospitals according to size: 100-299 beds, 300-599 beds, 600-899 beds, 900-1199 beds, and more than 1200 beds. The data consisted of 181 general hospitals with more than 100 beds, which included 28 public hospitals, 73 corporate hospitals, 64 university hospitals and 16 private hospitals. Of those, 87 hospitals were located in big cities and 94 hospitals in medium to small cities. This study used hospitals from the Korean Hospital Association, and data published in 2004. The collected data sample was analyzed using the SPSSWIN 12.0 version, and the study hypothesis was tested using regression analysis. The findings of this study are summarized as follows: Hypothesis 1 predicting a negative effect of the number of medical specialists on the number of out-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in all the hospital groups larger than the group of 100-299 beds. Hypothesis 2 predicting a negative effect of the number of medical specialists on the number of in-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds when compared to the group of 100-299 beds. Hypothesis 3 predicting a negative effect of the number of medical specialists on the volume of revenue per specialist was not supported. However, the analysis of dummy variable showed that the volume of revenue per specialist increased in the hospital groups of 600-899 beds, 900-1199 beds, and over 1200 beds, when compared to the group of 100-299 beds. Hypothesis 4 predicting a negative effect of the number of medical specialists on the average length of stay was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds, when compared to the group of 100-299 beds. Results of this study show that the number of the medical specialists per 100 beds is an important factor in hospital managerial performance. Most hospitals have tried to retain as many medical specialists as possible to keep the number of patients stable, to ensure adequate revenue, and to maintain efficient managerial performance. Especially, the big hospitals with greater number of beds and medical specialists have shown greater revenue per medical specialist despite the smaller number of patients per medical specialist. Findings of this study explains why hospitals in Korea are getting bigger.

• Annals of Coloproctology
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• v.34 no.6
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• pp.280-285
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• 2018

For many years, developmental and physiological differences have been known to exist between anatomic segments of the colorectum. Because of different outcomes, prognoses, and clinical responses to chemotherapy, the distinction between right colon cancer (RCC) and left colon cancer (LCC) has gained attention. Furthermore, variations in the molecular features and gut microbiota between right and LCCs have recently been a hot research topic. CpG island methylator phenotype-high, microsatellite instability-high colorectal cancers are more likely to occur on the right side whereas tumors with chromosomal instability have been detected in approximately 75% of LCC patients and 30% of RCC patients. The mutation rates of oncogenes and tumor suppressor genes also differ between RCC and LCC patients. Biofilm is more abundant in RCC patients than LLC patients, as are Prevotella, Selenomonas, and Peptostreptococcus. Conversely, Fusobacterium, Escherichia/Shigella, and Leptotrichia are more abundant in LCC patients compared to RCC patients. Distinctive characteristics are apparent in terms of molecular features and gut microbiota between right and LCC. However, how or to what extent these differences influence diverging oncologic outcomes remains unclear. Further clinical and translational studies are needed to elucidate the causative relationship between primary tumor location and prognosis.

• Mycobiology
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• v.49 no.6
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• pp.599-603
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• 2021

CRISPR/Cas9 genome editing systems have been established in a broad range of eukaryotic species. Herein, we report the first method for genetic engineering in pyogo (shiitake) mushrooms (Lentinula edodes) using CRISPR/Cas9. For in vivo expression of guide RNAs (gRNAs) targeting the mating-type gene HD1 (LeA1), we identified an endogenous LeU6 promoter in the L. edodes genome. We constructed a plasmid containing the LeU6 and glyceraldehyde-3-phosphate dehydrogenase (LeGPD) promoters to express the Cas9 protein. Among the eight gRNAs we tested, three successfully disrupted the LeA1 locus. Although the CRISPR-Cas9-induced alleles did not affect mating with compatible monokaryotic strains, disruption of the transcription levels of the downstream genes of LeHD1 and LeHD2 was detected. Based on this result, we present the first report of a simple and powerful genetic manipulation tool using the CRISPR/Cas9 toolbox for the scientifically and industrially important edible mushroom, L. edodes.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.8-14
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• 2019

Due to the development of computer technology, IoT technology is being used in various fields of industry, economy, medical service and education. However, multimedia information processed through IoT equipment is still one of the major issues in the application sector. In this paper, a big data protection model for users of IoT based IoT is proposed to ensure integrity of users' multimedia information processed through IoT equipment. The proposed model aims to prevent users' illegal exploitation of big data information collected through IoT equipment without users' consent. The proposed model uses signatures and authentication information for IoT users in a hybrid cryptographic method. The proposed model feature ensuring integrity and confidentiality of users' big data collected through IoT equipment. In addition, the user's big data is not abused without the user's consent because the user's signature information is encrypted using a steganography-based cryptography-based encryption technique.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.711-723
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• 2016

With the rapid development of both ubiquitous computing and the mobile internet, big data technology is gradually penetrating into various applications, such as smart traffic, smart city, and smart medical. In particular, smart medical, which is one core part of a smart city, is changing the medical structure. Specifically, it is improving treatment planning for various diseases. Since multiple treatment plans generated from smart medical have their own unique treatment costs, pollution effects, side-effects for patients, and so on, determining a sustainable strategy for treatment planning is becoming very critical in smart medical. From the sustainable point of view, this paper first presents a three-dimensional evaluation model for representing the raw medical data and then proposes a sustainable strategy for treatment planning based on the representation model. Finally, a case study on treatment planning for the group of "computer autism" patients is then presented for demonstrating the feasibility and usability of the proposed strategy.

• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.1-12
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• 2021

The purpose of this study is to analyze the medical ecosystem in the post-corona era. In addition, this study introduces a new medical CRM model that allows primary-level hospitals to overcome the economic difficulties and to occupy a competitive advantage in the post-corona era. The medical environment in the post-corona era is expected to be changed by non-face-to-face treatment, reinforcement of public medical care, the transformation of a medical system centered on the primary-level hospitals, and the use of AI and big data technologies. The medical CRM model presented in this study emphasizes the establishment of mutual customer relationships through close information exchange between patients, primary-level hospital, and the government. In the post-corona era, primary-level hospitals should not simply be approached as private hospital pursuing profitability. These should be reestablished as the hospitals that can provide public health care services while ensuring stable profitability.