• Journal of Society of Preventive Korean Medicine
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• v.3 no.2
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• pp.101-128
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• 1999

A study on disease treated at oriental medical treatment facilities (OMTF) and patients' satisfaction levels was conducted in order to figure out why the patients visited oriental medical doctors and the level of satisfaction of the patients for the services offered to them by oriental medical doctors. This study was performed from March 2 through May 31, 1998 by interviewing 1.532 persons living in major and small cities in korea. The results obtained were summarized as follows; 1. The general characteristics of subjects. The highest portion of each part was, 66.9% female, persons in the age group of over 60's 22.7%, high school graduated 34.9%, house wife 30.8%, The married 65.0%, Buddhist 36.9%, maj or city residents 60.2%, company covered insurance benefiter 39.0% and etc. 2. 40.5% of subjects visited OMTF for skeletal and connective tissue diseases. 21.5% for digestive system diseases. 16.2% for respiratory system diseases. 13.3% for circulatory system diseases and 9.0% for neurological problems. 3. 42.7% of males visited OMTF for skeletal and connective tissue diseases, which were the highest and respiratory system disorders, digestive system disorders, circulatory system disorders and neurological diseases in order. 39.4% of females visited OMTF for skeletal and connective tissue disorders which were the highest and other conditions such as digestive system, circulatory, respiratory, and neurological disorders in order. 4. The males with circulatory system disorders were treated by herbal medicine, combination of herbal medicine and acupuncture, only in order. The females with the some conditions above were treated by combination of herbal medicine and herbal medical and acupuncture only in order. The males and females with respiratory system and digestive system diseases were treated by herbal medicine, combination of herbal medicine and acupuncture only in order. But the males and females with skeletal and connective tissue diseases were by acupuncture are the highest in order. 5. The females and persons in the age group of over 60' s and house wife. the not married, the unhealthy persons, residents living in small cities, the persons with high income by medical treatments frequency in circulatory system diseases are the highest. 6, The females, middle school graduated and the married, persons in the age group of over 60's, unemployed, sales and service industry workers, Buddhists, major city residents, the unhealthy persons, the persons with middle income by medical treatments frequency in respiratory system diseases are the highest. 7. The females, persons in the age group of over 60's, under graduated or elementary school graduated, the unemployed and house wife, the unmarried, Buddhists, major city residents, the unhealthy persons, the persons with low income by medical treatments frequency in digestive system diseases are the highest. 8. The males, major city residents, old ages, under graduated or elementary school graduated, go earn officials, people grown in small city, the persons who had health insurance policies, the persons with low income, the unhealthy persons by medical treatments frequency in skeletal and connective tissue disorders diseases. 9. 50.8% of the respondents said that the treatments at the OMTF were very effective. 47.7% of them said that the treatments were effective. The males, persons in the age group of 40's, high school graduates, official workes, the married, the persons who did not have religion, major city residents, the persons who had health insurance policies, the persons with high income and the healthy persons said that the treatment effects at OMTF were satisfactory. 10. The patients' satisfaction rate for OMTF on each disease is, 1st. Musculo-Skeletal system(most satisfied), 2nd. the pregnancy & delivery complications, 3rd. Eye & ophthalmics, 4th. Respiratory system, 5th. Mental & bodily disorder, 6th. Digestive system and etc. 11. The factors affect OMTF are age, satisfaction for OMTF, current disease, religion, efficiency of Oriental Medicine, health condition and etc. This explained power of variable were 39.0%. 12. The satisfied factors for OMTF is correlate to educational level, and economical variables.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.59-70
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• 2020

This study was proceeded to empirically identify the start-up support policy as an element that affects the performance of the early start-ups and measure the effectiveness of the current start-up support policy, in order to suggest the direction future policies according to the study. To accomplish this the influence of the start-up support policy on the early start-ups was analyzed, and the differences according to the industry and growth stage of the start-ups, as the characteristics of the start-ups, were identified. The research subjects collected real data of 297 start-ups of the past three years that were selected for the Initial Start-Up Package project, and performed multiple regression analysis on the influence between variables, and hierarchical regression analysis on moderating effects. The summary of the study is as follows. First, as a result of identifying the influential relationship between the start-up support policy and the performance of the start-up, sales had made a significant impact on the start-up fund, start-up mentoring, and start-up infrastructure(space), while start-up education failed to show a significant effect on the increase in sales. In terms of employment, start-up mentoring was the only field that showed a significant influential relationship. Second, as a result of identifying the moderating effect of the start-up's industry and growth stage, the industry did not have a statistically significant influence, but the interactive effect was seen in start-up education. To be more specific in terms of the sales relationship of each industry, knowledge services turned out to be helpful in improving sales, while manufacturing turned out to be effective in improving sales regardless of being supported with start-up mentoring and start-up infrastructure (space). The sales relationship regarding the start-up growth stage was identified to be statistically significant. The preliminary stage was not statistically significant, while providing start-up mentoring and start-up funding were effective for start-up stage and growing stage, respectively. On the other hand, employment did not perform a significant influence on the start-up growth stage. This study analyzes the effectiveness the start-up support policy for early start-ups, identifies the need in differentiated support policies according to the characteristics of the start-ups, and suggests implications for the direction in which future policies should be made towards.

• Journal of Hospice and Palliative Care
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• v.8 no.2
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• pp.224-233
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• 2005

Purpose: This study was conducted to provide basic data for developing an effective strategy for cancer pain management by comparing the levels of barriers to pain management of metastatic or advanced cancer patient and their nurses. Methods: The subject of this study were 155 patients who were treated for metastatic or advanced cancer at one of three hospitals in Seoul from January 2004 to January 2005, and 153 nurses who take care of those patients. The levels of barriers to pain management were measured using a tool developed by Gunnarsdottir et al. (2002), 27 questions on a six point scale. The levels of stresses were measured using a tool modified from a stress response measurement reported by Goh Gyung-bong et al. (2000), 27 questions on a five point scale. The levels of barriers in cancer patients were analyzed using t-test and ANOVA, while the data obtained from patients and nurses were compared by t-test. Results: Higher levels of barriers to pain management were found in three groups: 'less than middle school,' 'not treated with anti-cancer chemotherapy,' and 'ECOG of 2.' The level (2.55) of barriers to pain management in the patient group was higher than that (1.76) of the nurse group. Both of the two groups had high levels of barriers in two variables: 'There is a danger of becoming addicted to pain medicine.' and 'Using pain medicine blocks your ability to know if you have any new pain.' There was not a significant difference in the levels of stresses between the two groups. Conclusion: It was found that, for effective cancer pain management practices, it would be necessary to provide cancer patients and their nurses with education and training about pain management and related barriers.

• Clinical and Experimental Pediatrics
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• v.49 no.7
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• pp.726-731
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• 2006

Purpose : We reported previously that intramuscular ketamine with adjunctive midazolam is more effective than ketamine alone in pediatric procedural sedation, but with limited satisfactory sedation by suboptimal ketamine dose. The optimal dose of intramuscular ketamine in children has never been studied in Korea. In this study, we investigated the effectiveness and adverse events of ketamine 4mg/kg with adjunctive midazolam in pediatric laceration repair. Methods : From Jan. 2005 to July 2005, we enrolled 60 children, aged 3 months-7 years, who needed laceration repair under sedation. After verbal consent from parents, patients were randomly assigned to KMA group(IM ketamine 4 mg/kg＋atropine 0.01 mg/kg＋intramuscular midazolam 0.05 mg/kg) or KA group(without midazolam). We compared both groups with the induction time, recovery time, total sedation time, efficacy of sedation, adverse effects, and the satisfaction score of treating physicians. Results : Potentially confounding variables, age, weight, injury site and anxiety score, were similar between groups. The induction time, recovery time and total sedation time were not different statistically. In KMA group, 90.9 percent of patients showed satisfactory sedation compared to 66.7 percent of KA group(P=0.02) and the occurrence rate of significant adverse effect was 0.0 percent and 37.0 percent respectively. Conclusion : We found adjunctive midazolam with ketamine doses of 4 mg/kg IM produced more effective, satisfactory sedation and less adverse effect than without midazolam in pediatric laceration repair. The emergence phenomenon(agitation during recovery) only occurred in 9 KA group patients. In spite of adverse effect, all patients recovered, were discharged and there were no reported delayed events.

• Journal of the Korean GEO-environmental Society
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• v.16 no.12
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• pp.45-52
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• 2015

With the rising interest in the environment, more attention on ecological restoration for damaged slope surface to restore its original state has been drawn. Generally, the most useful method is vegetation based spray work. This method uses green soil including sewage sludge, sawdust, paper sludge, and weathered granite soil. However, because there are neither accurate information nor test values about green soil, green soil is often lost by environmental factors such as rainfalls and strong winds. To solve the problem of green soil, it is necessary to prepare design standards about green soil, and conduct studies to deal with green soil loss in consideration of various variables including basic material property, soil quality of slope surface, and weather. This study was conducted in the mixture of green soil and eco-friendly soil stabilizer. With green soil, basic material property test and compaction test were conducted for the analysis on the basic characteristics of green soil. In the mixture with soil stabilizer at a certain ratio, we conducted shear strength test depending on the ratio in order to analyze the maximum shear strength, cohesion and the change in internal friction angles. Furthermore, in the mixture ratio of green soil and soil stabilizer, which is the same as the ratio in the shear strength test, an inclination of slope surface was made in laboratory for the analysis on erosion and germination rate. Finally, this study evaluated the most effective and economic mixing ratio of soil stabilizer to cope with neighboring environmental factors. According to the test, the shear strength of green soil increased up to 51% rely onto the mixing ratio of and a curing period, and its cohesion and internal friction angle also gradually increases. It is judged that the mixture of soil stabilizer was effective in improving shear strength and thereby increased the stability of green soil.

• Sleep Medicine and Psychophysiology
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• v.3 no.1
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• pp.85-95
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• 1996

Obstructive sleep apnea syndrome(OSAS) in childhood is unique and different n-om that in adulthood in several aspects, including pathophysiology, clinical features, diagnostic criteria, complications, management, and prognosis. Characteristic features of childhood OSAS in comparison with the adult form are the variety of severe complications such as developmental delay, more prominent behavioral and cognitive impairments, vivid cardiovascular symptoms, and increased death risk, warranting a special attention to the possible diagnosis of OSAS in children who snore. However, the childhood OSAS is often neglected and unrecognized. We, therefore, report a case of very severe OSAS in a 5-year-old boy who was sucessfully treated with continuous positive airway pressure(CPAP) treatment. Interestingly, the patient was comor-bid with the attention deficit hyperactivity disorder. Prior to the initial visit to us, adenotonsillectomy had been done at the age of 4 with no significant improvement of apneic symptoms and heavy snoring. On the initial diagnostic procedures, marked degree of snoring was audible even in the daytime wake state and the patient was observed to be very hyperactive. Increased pulmonary vascularity with borderline cardiomegaly was noted on chest X-ray. The baseline polysomnography revealed that the patient was very sleep-apneic and snored very heavily, with the respiratory disturbance index(RDI) of 46.9 per hour of sleep, the mean SaO2 of 78.8%, and the lowest SaO2 of 40.0%(the lowest detectable oxygen level by the applied oxymeter). The second night polysomnography was done for CPAP titration and the optimal pressure turned out to be $8.0\;cmH_2O$. The applied CPAP treatment was well tolerated by the patient and was found to be very effective in alleviating heavy snoring and severe repetitive sleep apneas. After 18 months of the CPAP treatment, the patient was followed up with nocturnal polysomnography(baseline and CPAP nights) and clinical examination. Sleep apneas were still present without CPAP on the baseline night. However, the severity of OSAS was significantly decreased(RDI of 15.7, mean SaO2 of 96.2%, and the lowest SaO2 of 83.0%), compared to the initial polysomnographic findings before initiation of long-term CPAP treatment. Wechsler intelligence tests done before and after the CPAP treatment were compared with each other and surprising improvement of intelligence(total 9 points, performance 16 points) was noted. Clinically he was found to be markedly improved in his attention deficit hyperactive behavior after CPAP treatment, but with minimal change of TOVA(test of variables of attention) scores except conversion of reaction time score into normal range. On the chest X-ray taken after 18 months of CPAP application, the initial cardiopulmonary abnormalities were not found at all. We found that the CPAP treatment in a young child is very effective, safe, and well-tolerated and also improves the co-morbid attention deficit hyperactive symptoms. Overall, the growth and development of the child has been facilitated with the long-term use of CPAP. Cardiovascular complications induced by OSAS have been also normalized with CPAP treatment. We suggest that early diagnosis and active treatment intervention of OSAS in children are crucial in preventing and ameliorating possible serious complications caused by repetitive sleep apneas and consequent hypoxic damage during sleep.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.