• Journal of Preventive Medicine and Public Health
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• v.25 no.1 s.37
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• pp.88-100
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• 1992

This study was conducted to assess the effects of implementation of the patient referral system started July 1st, 1989. A comparison on the pattern of outpatient services of the Departments of Internal Medicine, General Surgery, and Pediatrics at the Yeungnam University Hospital was conducted for each one year period before and after implementation of the patient referral system. The pre-implementation period was from July 1, 1988 to June 30, 1989 and the post-implementation period was from July 1, 1989 to June 30, 1990. The information used for this study was obtained from official forms, prepared by the Yeungnam University Hospital, and submitted to the Korean Medical Insurance Cooperatives. After implementation of the patient referral system, the number of outpatient cases in the Department of Internal Medicine decreased 36.1% from 9,669 cases to 6,181 cases a year. Cases in the Department of General Surgery decreased 23.7% from 1,864 cases to 1,422 cases a year. The number of cases in the Department of Pediatrics decreased 36.9% from 3,372 cases to 2,128 cases a year. After implementation of the patient referral system, the average age of cases in the Departments of Internal Medicine and General Surgery was 52.5 and 49.7 years old, respectively. This was a significant increase in comparison with the pre-implementation period. After implementation of patient referral system, the proportion of new outpatients in the Department of Internal Medicine decreased from 24.1% to 14.6%, the Department of General Surgery from 36.0% to 23.4%, and the Department of Pediatrics from 15.5% to 8.3%. The number of visits per case decreased significantly in the Department of Internal Medicine(from 1.74 to 1.61), but there was no significant change in the Departments of General Surgery and Pediatrics. The length of treatment per case increased significantly in all three departments(from 16.1 days to 19.3 days in the Department of Internal Medicine, from 12.0 days to 15.2 days in the Department General Surgery, and 8.9 days to 11.2 days in the Department of Pediatrics). The number of clinical tests per case increased significantly in the Department of Internal Medicine (from 2.2 to 2.5), in the Department of Pediatrics(from 0.8 to 1.1) and increased in the Department of General Surgery(from 6.4 to 6.6). The average medical cost per case decreased from 43,900 Won to 42,500 Won in the Department of Internal Medicine, while the cost increased from 75,900 Won to 78,500 Won in the Department of General Surgery and from 12,700 Won to 13,500 Won in the Department of Pediatrics. In case-mix, the chronic degenerative disease(i. e. hypertension, diabetes mellitus, angina pectoris, malignant neoplasm, and pulmonary tuberculosis) ranked higher and acute infectious diseases and simple cases(i. e. gastritis and duodenitis, haemorrhoids, anal fissure, carbuncle, acute URI, and bronchitis) ranked lower after implementation of the patient referral system compared to before implementation.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Life Science
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• v.19 no.4
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• pp.526-531
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• 2009

This study was conducted to identify the optimum condition for dehydration and storage for maintaining the seed vigor in pelleted carrot seeds. The water content of solid materials of un-pelleted seeds was 4.9% (F.W. basis), and that of pelleted seeds was 24%. When we dehydrated pelleted seeds for 3 hr at $25^{\circ}C$, $35^{\circ}C$, and $45^{\circ}C$, all seeds were dehydrated to 5% - the same level of water content as that before pelleting. Pelleted seeds did not show any significant difference in germination from un-pelleted seeds, and neither was there any significant time or germination difference between pelleted and un-pelleted seeds at the tested dehydration temperatures. However, the $T_{50}$ value of pelleted seeds increased about 3 days larger than that of un-pelleted seeds. Germination speed of pelleted seeds in which the dehydration period was prolonged at $45^{\circ}C$ was delayed compared to those prolonged at $25^{\circ}C$ or $35^{\circ}C$ for the same period. The optimum dehydration condition, which could be applied for large scale in the industry, was dehydration at $35^{\circ}C$ for 3 hr, and the optimum storage temperature which could maintain the seed performance was $5^{\circ}C$.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.8
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• pp.997-1011
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• 2017

This study aimed to evaluate the applicability of "food and nutrition standards for child care setting in the Seoul Metropolitan Government" using quantitative and qualitative surveys and lay the groundwork for future expansion. To evaluate the understanding and applicability of each item of the standards and utilize goodness of fit as an evaluation item to improve practicality of the child care center's health foodservice, we surveyed 82 child care centers, kindergartens, and community children centers in Seoul by e-mail through the Seoul Metropolitan Government in November 2015. Using focus group interviews with directors of child care centers and kindergartens as well as dieticians of child care support centers who had conducted public foodservice in Seoul, the appropriateness of each standard was finally reviewed, and amendments to extend this standard were derived. Results of the survey show an understanding of 86.9% and an applicability of 80.7%. Moreover, the applicability of the evaluation items for assessing improvement of health foodservice practices at the child care center was 82.6%. Qualitative evaluation of the applicability of standards through focus group interviews identified four factors for extended application: necessity of a guideline for separate food ingredient suppliers, conformity of the basis and purpose of the item, consideration of contextual situations, and necessity of establishing a Seoul-level foundation. Therefore, guidelines for the standards should be suggested for those providing public food services for children. Furthermore, separate guidelines categorized by items that food ingredient suppliers need to provide should be mandatory or recommended. Evaluation of the applicability of the standards suggests that revising and supplementing the guidelines in order to extend the standards can contribute to health promotion and a sustainable environment for children using public foodservice. Follow-up studies and supports are needed for sustainable food and nutrition management programs.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Korean Journal of Health Education and Promotion
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• v.24 no.5
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• pp.23-37
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• 2007

Background: Even though cervical cancer poses a significant global cancer burden, successful implementations for early detection offer an opportunity to prevent deaths and reduce the cancer burden. In Korea, cervical cancer is the most prevalent type of cancer among adult women, but it is one of the few cancers in which a consensus-approved screening test exists for early diagnosis, Pap test, that can be combined with highly efficacious treatment regimens for early-stage disease. Purpose: This study was carried out to identify the cognitive-behavioral factors associated with cervical cancer screening behavior among adult women, aged 40 to 59, and to develop tailored messages and to evaluate the effectiveness of stage-matched educational program. Method: A total of 283 women who aged 40 years or older was recruited in Seoul, from September, 1st to November, 14th, 2003. The intervention group (N=162) and the control group (N=121) were selected from five sub-districts in Seocho-gu, Seoul. Building on the TTM, a quasi-experimental study was conducted to test the effectiveness of stages-matched intervention addressed at the five stages of cervical cancer screening behavior. Women in the intervention group were randomly assigned to one of two conditions, internet or postal services. Results: In our results, 88.9% of participants had received a Pap test at least once in their life-time, and 65.4% had got it in the past two years. With regard to cognitive-behavioral factors, the stages-matched educational program increased attitude and process of change for cervical cancer screening. The percentage changed was the largest in maintenance stage. With regard to delivery methods for tailored messages, the print materials were more effective at increasing screening adherence than the e-mail. Whereas the postal service group showed remarkable the change of behavior stage, the internet service group did not. Also it was not shown any difference of the satisfaction with stages-matched educational program between internet and postal service groups. Conclusion: This study suggested that cervical cancer screening behavior could be changed by tailored messages which had developed with cognitive-behavioral factors. The stages-matched educational program was effective to promote the screening adherence for cervical cancer.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.28-36
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• 2009

While existing literature has focused extensively on the strengths and weaknesses of the Chaebol and their ownership and governance, there have been few studies of Korean non-Chaebol firms. However, Lee, Lee and Pennings (2001) did not specifically investigate the competitive strategies that non-Chaebol firms use to survive against the Chaebol in the domestic Korean market. The motivation of this paper is to document, through four exploratory case studies, the successful competitive strategies of non-Chaebol Korean companies against the Chaebol and then offer some propositions that may be useful to other entrepreneurial firms as well as public policy makers. Competition and cooperation as conceptualized by product similarity and cooperative inter.firm relationship respectively, are major dimensions of firm.level marketing strategy. From these two dimensions, we develop the following $2{\times}2$ matrix, with 4 types of competitive strategies for non-Chaebol companies against the Chaebol (Fig. 1.). The non-Chaebol firm in Cell 1 has a "me-too" product for the low-end market while conceding the high-end market to a Chaebol. In Cell 2, the non-Chaebol firm partners with a Chaebol company, either as a supplier or complementor. In Cell 3, the non-Chaebol firm engages in direct competition with a Chaebol. In Cell 4, the non-Chaebol firm targets an unserved part of the market with an innovative product or service. The four selected cases such as E.Rae Electronics Industry Company (Co-exister), Intops (Supplier), Pantech (Competitor) and Humax (Niche Player) are analyzed to provide each strategy with richer insights. Following propositions are generated based upon our conceptual framework: Proposition 1: Non-Chaebol firms that have a cooperative relationship with a Chaebol will perform better than firms that do not. Proposition 1a; Co-existers will perform better than Competitors. Proposition 1b: Partners (suppliers or complementors) will perform better than Niche players. Proposition 2: Firms that have no product similarity with a Chaebol will perform better than firms that have product similarity. Proposition 2a: Partners (suppliers or complementors) will perform better than Co.existers. Proposition 2b: Niche players will perform better than Competitors. Proposition 3: Niche players should perform better than Co-existers. Proposition 4: Performance can be rank.ordered in descending order as Partners, Niche Players, Co.existers, Competitors. A team of experts was constituted to categorize each of these 216 non-Chaebol companies into one of the 4 cells in our typology. Simple Analysis of Variance (ANOVA) in SPSS statistical software was used to test our propositions. Overall findings are that it is better to have a cooperative relationship with a Chaebol and to offer products or services differentiated from a Chaebol. It is clear that the only profitable strategy, on average, to compete against the Chaebol is to be a partner (supplier or complementor). Competing head on with a Chaebol company is a costly strategy not likely to pay off for a non-Chaebol firm. Strategies to avoid head on competition with the Chaebol by serving niche markets with differentiated products or by serving the low-end of the market ignored by the Chaebol are better survival strategies. This paper illustrates that there are ways in which small and medium Korean non-Chaebol firms can thrive in a Chaebol environment, though not without risks. Using different combinations of competition and cooperation firms may choose particular positions along the product similarity and cooperative relationship dimensions to develop their competitive strategies-co-exister, competitor, partner, niche player. Based on our exploratory case-study analysis, partner seems to be the best strategy for non-Chaebol firms while competitor appears to be the most risky one. Niche players and co-existers have intermediate performance, though the former do better than the latter. It is often the case with managers of small and medium size companies that they tend to view market leaders, typically the Chaebol, with rather simplistic assumptions of either competition or collaboration. Consequently, many non-Chaebol firms turn out to be either passive collaborators or overwhelmed competitors of the Chaebol. In fact, competition and collaboration are not mutually exclusive, and can be pursued at the same time. As suggested in this paper, non-Chaebol firms can actively choose to compete and collaborate, depending on their environment, internal resources and capabilities.