• Korean Journal of Hospice Care
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• v.2 no.2
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• pp.28-48
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• 2002

The advances of medical technologies have not only prolonged human life span, but also extended suffering period for the patients with incurable medical diseases. Hospice movement was developed to help these patients keep dignity and lives peaceful at the end of their life. Since many patients prefer to spend the last moment of life at home with their family, hospice home care has become very popular worldwide. The purpose of this study for a promotion and development of hospice home care in Korea, and features basic research on medical profession's knowledge and attitudes to hospice home care. This study which was used for the research questionnaires developed by the researcher that were answered by 100 physicians and 127 nurses in a general hospital. Data were collected from April 22, 2002 to May 10, 2002. The SPSS was used to make a comparative analysis of the frequency, percentile, ANOVA, and x2-test. The results of the study were as follows; 1.The medical profession showed high level of knowledge of the definition and philosophy of hospice. However, the physician group of the examinees showed insufficient knowledge of the fact that hospice care includes bereavement care, while the nurse group's response to the same question showed a significant difference(x2=10.752, p=.001). 2.For whom the hospice home care is provided, 95.6% of the respondents showed very high level of knowledge as answering that the incurable terminal illness patients and their families are the beneficiaries of hospice care. The respondents counted nurses, volunteers, pastors, physicians and social workers, consecutively, as hospice care providers. More nurse were positive toward pastors than physicians in regarding as a hospice care provider by a significant difference(x2=11.634, p=.001). 3.For when to referral hospice home care to the patients, only 34.2% answered that patients with less than 6 months of survival time are advised to receive hospice care, reflecting very low level of knowledge. 23.0% of the physicians and 48.0% of the nurses answered that hospice care should be provided when death is imminent, making a significant difference between the two groups(x2=6.413, p=.000). 4.To promote hospice activities, 87.2% pointed out that it is crucial to make general people, including those engaging in the medical field, more aware of hospice. 79.7% answered that a national hospice management should be developed, marking a significant difference between the physician group and nurse group(x2=10.485, p=.001). 5.Advantages of hospice home care are 87.2% responded that patients can have better rest at home receiving hospice home care. Economical merit was brought forward as one of the advantages also, where there was a significant difference between the physicians group and nurse group(x2=7.009, p=.008). 6.The medical professions' attitude to hospice home care are 92.8% of the physicians answered that they would advise incurable terminally ill patients to be discharged from hospital, with 44.3% of them advising the patients to receive hospice home care after leaving the hospital. From the nurses' point of view, 20.9% of the terminally ill patients are being referred to hospice home care after discharge, which makes a significant difference from the physicians' response(x2=19.121, p=.001). 7. 30.6% of physicians have referred terminally ill patients to hospice home care, 75.9% of whom were satisfied with their decision. Those physicians who have never referred their patients to hospice home care either did not know how to do it(66.7%) or were afraid of losing trust by giving the patients an impression of giving up(27.3%). 94.9% of the physicians responded that they would refer their last stage patients to a doctor who is involving palliative care. 8.Only 36.2% of nurses have suggested to physicians that refer the terminally ill patients discharged from the hospital to hospice home care. Once suggested, 95.8% of the physicians have accepted the suggestion. Nurses were reluctant to suggest hospice home care to the physicians, as 48.8% of the nurses said they did not want to. From the result of this study the following conclusion can be drawn, the medical profession's awareness of general hospice care has been increased greatly compared to the results of the previously performed studies. However, this study result also shows that their knowledge of hospice home care is not good enough yet. There is a need for high recommended that medical education institute and develop regular courses on various types of hospice care. Medical field training courses for physicians and nurses will be very helpful as well. It is also important to train hospice experts such as palliative physicians and develop a national hospice management urgently in order to improve the hospice care in Korea.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.2
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• pp.1-14
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• 2021

Ssangsanjae was created in the mid-1800s, It is located at Jiri Mountain to the north and the Seomjin River to the south. This garden has not changed much even though it has passed through the sixth generation since its creation, so it still retains the features of a private garden in the late Joseon Dynasty. This study focused on the changing landscape of Ssangsanjae as a historical garden; through field surveys, interviews and analysis of builder's collection, boards and couplets. Ssangsanjae is largely classified into inner and outer gardens, and the inner is divided into an entry space, a residential space, and a backyard. The backyard consists of Seodangchae, it's garden, Gyeongamdang, and swimming pool, and is connected to the Sado Reservoir area, which is the outer garden. The distinct vegetation landscape of Ssangsanjae are a 13,000m² bamboo and green tea field, Peony(Paeonia suffruticosa Andr. and Paeonia lactiflora var. trichocarpa(Bunge) Stern) planted on both sides of the road that crosses the lawn, the view through a frame(額景) shown by the twisted branches of Camellia and Evergreen spindletree, and a fence made of Trifolia Orange(Poncirus trifoliata) and Bamboo. Ssangsanjae stands out for its spatial composition and arrangement in consideration of the topography and native vegetation. The main building was named by the descendants based on the predecessor's Aho(pseudonym), and it is the philosophical view of the predecessors who tried to cultivate the younger students without going up on the road. The standing stone and white boundary stone built by Mr. Oh Ju Seok are Ssangsanjae's unique gardening facilities. The stone chairs, and swimming pool which were created by the current owner for the convenience of families and visitors also make a distinctive landscape. Ssangsanjae, for residents, was a place for living, exchanging friendships, training himself and seculusion, for children was a place for learning, but now is 'the private garden' where many people can heal themselves. Over the 200 years, the landscape of Ssangsanjae's inner and outer gardens experienced large and small changes. As such, it is necessary to recognize the historical gardens with changing properties as a living heritage. This study is significant in that, as the first study to approach Ssangsanjae in the view of landscape research, it provides basic data on Ssangsanjae as a destination of garden tourism.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.106-121
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• 2011

The number of World Natural Heritage Sites is smaller than that of World Cultural Heritage Sites. As of 2010, the total number of natural sites was 180, which is less than 1/3 of all cultural sites. The reason why the number of natural sites is smaller can be attributed to the evaluating criteria of OUV(outstanding universal value). Only 9 fossil related sites were designated as World Heritage Sites among 180 Natural Sites. This study compares their OUVs including the academic value and characteristics of the 9 World Heritage Sites to provide data and reference for KCDC(Korean Cretaceous Dinosaur Coast) to apply as a World Natural Heritage Site. This study was carried out to obtain information and data on the Wadi Al-Hitan of Egypt which was designated as a World Natural Heritage Site. The study includes field investigation for whale fossils, interviews of site paleontologists and staff, and inspections of facilities. Three factors can likely be attributed to its successful management and operating system. First, there is a system for comprehensive research and a monitoring plan. Secondly, experts have been recruited and hired and professional training for staff members has been done properly. Finally, the Wadi Al-Hitan has developed local resources with specialized techniques for conservation and construction design, which matched well with whale fossils and the environment at the site. The Wadi Al-Hitan put a master plan into practice and achieved goals for action plans. To designate a future World Natural Heritage Site in Korea, it is important to be recognized by international experts including IUCN specialists as the best in one's field with OUV. Full-time regular-status employees for a research position are necessary from the preparation stage for the UNESCO World Heritage Site. Local government and related organizations must do their best to control monitoring plans and to improve academic value after the UNESCO World Heritage Site designation. As we experienced during the designation process of Jeju Volcanic Island and Lava Tubes as the first Korean World Natural Heritage Site, participation by various scholars and specialists need to be in harmony with active endeavors from local governments and NGOs.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.1
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• pp.40-53
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• 2015

To train the manpower to meet the requirements of the industrial field, the introduction of the National Qualification Frameworks(hereinafter referred to as NQF) was determined in 2001 by National Competency Standards(hereinafter referred to as NCS) centrally of the Office for Government Policy Coordination. Also, for landscape architecture in the construction field, the "NCS -Landscape Architecture" pilot was developed in 2008 to be test operated for 3 years starting in 2009. Especially, as the 'realization of a competence-based society, not by educational background' was adopted as one of the major government projects in the Park Geun-Hye government(inaugurated in 2013) the NCS system was constructed on a nationwide scale as a detailed method for practicing this. However, in the case of the NCS developed by the nation, the ideal job performing abilities are specified, therefore there are weaknesses of not being able to reflect the actual operational problem differences in the student level between universities, problems of securing equipment and professors, and problems in the number of current curricula. For soft landing to practical curriculum, the process of clearly analyzing the gap between the current curriculum and the NCS must be preceded. Gap analysis is the initial stage methodology to reorganize the existing curriculum into NCS based curriculum, and based on the ability unit elements and performance standards for each NCS ability unit, the discrepancy between the existing curriculum within the department or the level of coincidence used a Likert scale of 1 to 5 to fill in and analyze. Thus, the universities wishing to operate NCS in the future measuring the level of coincidence and the gap between the current university curriculum and NCS can secure the basic tool to verify the applicability of NCS and the effectiveness of further development and operation. The advantages of reorganizing the curriculum through gap analysis are, first, that the government financial support project can be connected to provide quantitative index of the NCS adoption rate for each qualitative department, and, second, an objective standard is provided on the insufficiency or sufficiency when reorganizing to NCS based curriculum. In other words, when introducing in the subdivisions of the relevant NCS, the insufficient ability units and the ability unit elements can be extracted, and the supplementary matters for each ability unit element per existing subject can be extracted at the same time. There is an advantage providing directions for detailed class program and basic subject opening. The Ministry of Education and the Ministry of Employment and Labor must gather people from the industry to actively develop and supply the NCS standard a practical level to systematically reflect the requirements of the industrial field the educational training and qualification, and the universities wishing to apply NCS must reorganize the curriculum connecting work and qualification based on NCS. To enable this, the universities must consider the relevant industrial prospect and the relation between the faculty resources within the university and the local industry to clearly select the NCS subdivision to be applied. Afterwards, gap analysis must be used for the NCS based curriculum reorganization to establish the direction of the reorganization more objectively and rationally in order to participate in the process evaluation type qualification system efficiently.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Korean Nurse
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• v.29 no.2
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• pp.48-65
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• 1990

The government has adopted a policy to introduce Home Health Care Program, and has established a three stage plan to implement it. The three stage plan is : First, to amend Article 54 (Nurses for Different Types of Services) of the Regulations for Implementing the Law of Medical Services; Second, to tryout the new system through pilot projects established in public hospitals and clinics; and third, to implement at all hospitals and equivalent medical institutions. In accordance with the plan, the Regulation has been amend and it was promulgated on January 9,1990, thus establishing a legal ground for implementing the policy. Subsequently, however, the Medical Association raised its objection to the policy, causing a delay in moving into the second stage of the plan. Under these circumstances, a study was conducted by collecting and evaluating the opinions of physicians, nurses, non-medical personnel and patients on the need and expected result from the home health care for the purpose of help facilitating the implementation of the new system. As a result of this study, it was revealed that: 1. Except the physicians, absolute majority of all other three groups - nurses, non-medical personnel and patients -gave positive answers to all 11 items related to the need for establishing a program for Home Health Care. Among the physicians, the opinions on the need for the new services were different depending on their field of specialty, and those who have been treating long term patients were more positive in supporting the new system. 2. The respondents in all four groups held very positive view for the effectiveness and the expected result of the program. The composite total of scores for all of 17 items, however, re-veals that the physicians were least positive for the- effectiveness of the new system. The people in all four groups held high expectation on the system on the ground that: it will help continued medical care after the discharge from hospitals; that it will alleviate physical and economic burden of patient's family; that it will offer nursing services at home for the patients who are suffering from chronic disease, for those early discharge from hospital, or those who are without family members to look after the patients at home. 3. Opinions were different between patients( who will receive services) and nurses (who will provide services) on the types of services home visiting nurses should offer. The patients wanted "education on how to take care patients at home", "making arrangement to be admitted into hospital when need arises", "IV injection", "checking blood pressure", and "administering medications." On the other hand, nurses believed that they can offer all 16 types of services except "Controlling pain of patients", 4. For the question of "what types of patients are suitable for Home Health Care Program; " the physicians, the nurses and non-medical personnel all gave high score on the cases of "patients of chronic disease", "patients of old age", "terminal cases", and the "patients who require long-term stay in hospital". 5. On the question of who should control Home Health Care Program, only physicians proposed that it should be done through hospitals, while remaining three groups recommended that it should be done through public institutions such as public health center. 6. On the question of home health care fee, the respondents in all four groups believed that the most desireable way is to charge a fixed amount of visiting fee plus treatment service fee and cost of material. 7. In the case when the Home Health Care Program is to be operated through hospitals, it is recommended that a new section be created in the out-patient department for an exclusive handling of the services, instead of assigning it to an existing section. 8. For the qualification of the nurses for-home visiting, the majority of respondents recommended that they should be "registered nurses who have had clinical experiences and who have attended training courses for home health care". 9. On the question of if the program should be implemented; 74.0% of physicians, 87.5% of non-medical personnel, and 93.0% of nurses surveyed expressed positive support. 10. Among the respondents, 74.5% of -physicians, 81.3% of non-medical personnel and 90.9% of nurses said that they would refer patients' to home health care. 11. To the question addressed to patients if they would take advantage of home health care; 82.7% said they would if the fee is applicable to the Health Insurance, and 86.9% said they would follow advises of physicians in case they were decided for early discharge from hospitals. 12. While 93.5% of nurses surveyed had heard about the Home Health Care Program, only 38.6% of physicians surveyed, 50.9% of non-medical personnel, and 35.7% of patients surveyed had heard about the program. In view of above findings, the following measures are deemed prerequisite for an effective implementation of Home Health Care Program. 1. The fee for home health care to be included in the public health insurance. 2. Clearly define the types and scope of services to be offered in the Home Health Care Program. 3. Develop special programs for training nurses who will be assigned to the Home Health Care Program. 4. Train those nurses by consigning them at hospitals and educational institutions. 5. Government conducts publicity campaign toward the public and the hospitals so that the hospitals support the program and patients take advantage of them. 6. Systematic and effective publicity and educational programs for home heath care must be developed and exercises for the people of medical professions in hospitals as well as patients and their families. 7. Establish and operate pilot projects for home health care, to evaluate and refine their programs.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.