Purpose : Medicolegal problems start when the patient asserts the mistake of doctor and doctor does not accept it. The purpose of this study is to assess the actual condition of medicolegal problems and to provide solutions of medicolegal problems in the pediatric field. Methods : There is not official statistical data about medicolegal problems in our country. We gathered data of legal insurance program of Korean Medical Association(KMA) and court cases and other fragmentary data. Results : Between 1981 and 1995, of total 2,338 cases reported to legal problem insurance program of KMA, most common ones were 748 cases of obstetrics and gynecology. Pediatric case was ranked at the 5th, 74 cases(3.1%). According to analysis of 41 medicolegal cases' after 1990, maltreatment of patient had the highest incidence of 14 cases, injection and medication were related to 12 cases, misdiagnosis was 9 cases, patient management were related to 4 cases, and others were 2 cases. The trial result of the medicolegal cases was that 31 cases were compensated, and 8 cases were defeated, and 2 cases were still in the process. Conclusion : The aspect of medical legal problem has the tendency of radicalism and systematization. This brings an economic destitution in the patient and gives damage to a doctor. In order to reduce medicolegal problem, doctor should offer a duty of explanation and efforts to his best to satisfy patient and endeavor to make an intimate doctor-patient relationship.
Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.
The study attempted to estimate the optimal facility capacity by combining renewable energy sources that can be connected with gas CHP in industrial complexes. In particular, we reviewed industrial complexes subject to energy use plan from 2013 to 2016. Although the regional designation was excluded, Sejong industrial complex, which has a fuel usage of 38 thousand TOE annually and a high heat density of
As most G-20 countries expect medical spending to grow rapidly over the next few decades, the burden of healthcare costs continues to grow globally due to an increase in the elderly population and chronic illnesses, and the ongoing quality improvement of health care services. However, under the rapidly changing technological environment of healthcare and IT convergence, the problem may become even bigger if not properly recognized and not properly prepared. In the context of the paradigm shift and the increasing problem of the medical field, complex responses in technical, institutional and business aspects are urgently needed. The key is to derive a business model that is appropriate for businesses that integrate IT in the medical field. With the arrival of the era of the 4th industrial revolution, new technologies such as Internet of Things have been applied to eHealthcare, and the need for new business models has emerged.In the e-healthcare of the Internet era, it became a traditional firm-based business model. However, due to the characteristics of dynamics and complexity of things Internet in the Internet of things, A business ecosystem-based approach is needed. In this paper, we present and analyze the major success factors of the ecosystem based on the 3 - layer structure of the e - healthcare business ecosystem as a result of research on e - healthcare business ecosystem based on emerging technology such as Internet of things. The three-layer business ecosystem was defined as (1) Infrastructure Layer, (2) Character Layer, and (3) Stakeholder Layer. As the key success factors for the eHealthCare business ecosystem, the following four factors are suggested: (1) introduction of the iHealthcare concept, (2) expansion of the business ecosystem, (3) business ecosystem change process innovation, and (4) business ecosystem leadership innovation.
For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.
Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.
Along with the development of scientific technologies, health care has been growing remarkably, and as the social life quality improves with increasing interest in health, the demand for medical service is rapidly increasing. However, medical accident and medical dispute also are rapidly increasing due to various factors such as, increasing sense of people's right, lack of understanding in the nature of medical practice, over expectation on medical technique, commercialize medical supply system, moral degeneracy and unawareness of medical jurisprudence by doctors, widespread trend of mutual distrust, and lack of systematized device for solution of medical dispute. This study analysed 30 cases of civil suit in the year between 1994 to 2004, which were selected among the medical dispute cases in dental field with the judgement collected from organizations related to dentistry and department of oral medicine, Yonsei university dental hospital. The following results were drawn from the analyses: 1. The distribution of year showed rapid increase of medical dispute after the year 2000. 2. In the types of medical dispute, suit associated with tooth extraction took 36.7% of all. 3. As for the cause of medical dispute, uncomfortable feeling and dissatisfaction with the treatment showed 36.7%, death and permanent damage showed 16.7% each. 4. Winning the suit, compulsory mediation and recommendation for settlement took 60.0% of judgement result for the plaintiff. 5. For the type of medical organization in relation to medical dispute, 60.0% was found to be the private dental clinics, and 30.0% was university dental hospitals. 6. For the level of trial, dispute that progressed above 2 or 3 trials was of 30.0%. 7. For the amount of claim for damage, the claim amounting between 50 million to 100 million won was of 36.7%, and that of more than 100 million won was 13.3%, and in case of the judgement amount, the amount ranging from 10 million to 30 million won was of 40.0%, and that of more than 100 million won was of 6.7%. 8. For the number of dentist involved in the suit, 26.7% was of 2 or more dentists. 9. For the amount of time spent until the judgement, 46.7% took 11 to 20 months, and 36.7% took 21 to 30 months. 10. For medical malpractice, 46.7% was judged to be guilty, and 70% of the cases had undergone medical judgement or verification of the case by specialists during the process of the suit. 11. In the lost cases of doctors(18 cases), 72.2% was due to violence of carefulness in practice and 16.7% was due to missing of explanation to patient. Medical disputes occurring in the field of dentistry are usually of relatively less risky cases. Hence, the importance of explanation to patient is emphasized, and since the levels of patient satisfaction are subjective, improvement of the relationship between the patient and the dentist and recovery of autonomy within the group dentist are essential in addition to the reduction of technical malpractice. Moreover, management measure against the medical dispute should be set up through complement of the current doctors and hospitals medical malpractice insurance which is being conducted irrationally, and establishment of system in which education as well as consultation for medical disputes lead by the group of dental clinicians and academic scholars are accessible.
Background : Idiopathic pulmonary fibrosis (IPF) is a diffuse inflammatory and fibrosing process that occurs within the interstitium and alveolus of the lung with invariably poor prognosis. The major problem in management of IPF results from the variable rate of disease progression and the difficulties in predicting the response to therapy. The purpose of this retrospective study was to evaluate the short-term efficacy of steroid and immunosuppressive therapy for IPF and to identify the pre-treatment determinants of favorable response. Method : Twenty patients of IPF were included. Diagnosis of IPF was proven by thoracoscopic lung biopsy and they were presumed to have active progressive disease. The baseline evaluation in these patients included clinical history, pulmonary function test, bronchoalveolar lavage (BAL), and chest high resolution computed tomography (HRCT). Fourteen patients received oral prednisolone treatment with initial dose of 1mg/kg/day for 8 to 12 weeks and then tapering to low-dose prednisolone (0.25mg/kg/day). Six patients who previously had experienced significant side effects to steroid received 2mg/kg/day of oral cyclophosphamide with or without low-dose prednisolone. Follow-up evaluation was performed after 6 months of therapy. If patients met more than one of followings, they were considered to be responders : (1) improvement of more than one grade in dyspnea index, (2) improvement in FVC or TLC more than 10% or improvement in DLco more than 20% (3) decreased extent of disease in chest HRCT findings. Result : One patient died of extrapulmonary cause after 3 month of therapy, and another patient gave up any further medical therapy due to side effect of steroid. Eventually medical records of 18 patients were analyzed. Nine of 18 patients were classified into responders and the other nine patients into nonresponders. The histopathologic diagnosis of the responders were all nonspecific interstitial pneumonia (NSIP) and that of nonresponders were all usual interstitial pneumonia (UIP) (p<0.001). The other significant differences between the two groups were female predominance (p<0.01), smoking history (p<0.001), severe grade of dyspnea (p<0.05), lymphocytosis in BAL fluid ( shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel ( shows various market conditions captured by the two consumer heterogeneities. (a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in (c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers.
Different Look, Different Feel: Social Robot Design Evaluation Model Based on ABOT Attributes and Consumer Emotions
(각인각색, 각봇각색: ABOT 속성과 소비자 감성 기반 소셜로봇 디자인평가 모형 개발)
The Jurisdictional Precedent Analysis of Medical Dispute in Dental Field
(치과임상영역에서 발생된 의료분쟁의 판례분석)
Short-Term Efficacy of Steroid and Immunosuppressive Drugs in Patients with Idiopathic Pulmonary Fibrosis and Pre-treatment Factors Associated with Favorable Response
(특발성폐섬유화증에서 스테로이드와 면역억제제의 단기 치료효과 및 치료반응 예측인자)
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