• Clinical and Experimental Pediatrics
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• v.55 no.4
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• pp.121-127
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• 2012

Purpose: Improved survival of patients with childhood acute lymphoblastic leukemia (ALL) has drawn attention to the potential for late consequences of previous treatments among survivors, including metabolic syndrome. In this study, we evaluated changes in 3 parameters, namely, random blood glucose, body mass index (BMI), and Z score for BMI (Z-BMI), in children with ALL during chemotherapy and after completion of treatment. Methods: Patients newly diagnosed with ALL from January, 2005 to December, 2008 at Saint Mary's Hospital, The Catholic University of Korea, who completed treatment with chemotherapy only were included (n=107). Random glucose, BMI, and Z-BMI were recorded at 5 intervals: at diagnosis, before maintenance treatment, at completion of maintenance treatment, and 6 and 12 months after completion of maintenance treatment. Similar analyses were conducted on 2 subcohorts based on ALL risk groups. Results: For random glucose, a paired comparison showed significantly lower levels at 12 months post-treatment compared to those at initial diagnosis ($P$ <0.001) and before maintenance ($P$ <0.001). The Z-BMI score was significantly higher before maintenance than at diagnosis ($P$ <0.001), but decreased significantly at the end of treatment ($P$ <0.001) and remained low at 6 months ($P$ <0.001) and 12 months ($P$ <0.001) post-treatment. Similar results were obtained upon analysis of risk group-based subcohorts. Conclusion: For a cohort of ALL patients treated without allogeneic transplantation or cranial irradiation, decrease in random glucose and Z-BMI after completion of chemotherapy does not indicate future glucose intolerance or obesity.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.101-106
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• 2017

Based on the previous reports that majority of teachers have negative perceptions on food additives, a teacher training program called 'Let's teach food additives correctly' was developed and applied to improve teachers' unbalanced perception on food additives and let them teach students with sound scientific background. The 15-hours training program consisted of understanding of food safety and food additives, education materials on food additives, development of teaching-learning plan, meeting with professionals from Ministry of Food and Drug Safety (MFDS), visiting MFDS labs, application to school class, and evaluation. A total of 63 teachers participated in the training through 2 sessions conducted in August 2016. As a result, teachers showed high satisfaction rates (4.2~4.5 in 5.0-Likert scale) and 91.5% answered the training helpful for the school class. Although their initial intention to participate the training was to know the details of negative intake effects of food additives, their such perception was totally changed in addition, they suggested a continuous training for teachers and immediate correction of incorrect information in school textbooks. Also, post-training education for 1,172 students by these teachers appeared to improve the understanding of and the native perceptions on food additives significantly (p < 0.001). Above results showed that the training program could solve the problem of transmitting unbalanced information on food additives to students by training teachers, and such channel could be used to facilitate food-related risk communication.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.185-196
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• 2023

This paper investigates the effects of entrepreneurs' cognitive biases on business opportunity evaluation, given their strong entrepreneurial spirit, which is characterized by innovation, proactivity, and risk-taking. When making decisions related to business activities, entrepreneurs typically make rational judgments based on their knowledge, experience, and the advice of external experts. However, in situations of extreme stress or when quick decisions are required, they often rely on heuristics based on their cognitive biases. In particular, we often see cases where entrepreneurs fail because they make decisions based on heuristics in the process of evaluating and selecting new business opportunities that are planned to guarantee the growth and sustainability of their companies. This study was conducted in response to the need for research to clarify the effects of entrepreneurs' cognitive biases on new business opportunity evaluation, given that the cognitive biases of entrepreneurs, which are formed by repeated successful experiences, can sometimes lead to business failure. Although there have been many studies on the effects of cognitive biases on entrepreneurship and opportunity evaluation among university students and general people who aspire to start a business, there have been few studies that have clarified the relationship between cognitive biases and social networks among entrepreneurs. In contrast to previous studies, this study conducted empirical surveys of entrepreneurs only, and also conducted research on the relationship with social networks. For the study, a survey was conducted using a parallel survey method using online mobile surveys and self-report questionnaires from 150 entrepreneurs of small and medium-sized enterprises. The results of the study showed that 'overconfidence' and 'illusion of control', among the independent variables of entrepreneurs' cognitive biases, had a statistically significant positive(+) effect on business opportunity evaluation. In addition, it was confirmed that the moderating variable, social network, moderates the effect of overconfidence on business opportunity evaluation. This study showed that entrepreneurs' cognitive biases play a role in the process of evaluating and selecting new business opportunities, and that social networks play a role in moderating the structural relationship between entrepreneurs' cognitive biases and business opportunity evaluation. This study is expected to be of great help not only to entrepreneurs, but also to entrepreneur education and policy making, by showing how entrepreneurs can use cognitive biases in a positive way and the influence of social networks.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3336-3340
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• 2010

This study has been carried out against hospitals with less than 100 beds, which were excluded from the previous study on the discharged patients from January 1 to December 31, 2007. To analyze the discharged patients, the general characteristics of discharged patients, means of payment for medical costs, type of disease and surgery and current status of the use of medical services have been investigated based on the medical records. During the year of 2007, the number of discharged patients from the hospitals with less than 100 beds reached 4,697,095 (9.7% of total population). In other words, 9,693 people were discharged from a hospital per 100,000 populations with 9.8 days in terms of annual mean length of hospitalization. The number of patients who returned home after hospitalization reached 4,538,861 (male: 1,784,041, female: 2,754,821) while 119,378 patients were evacuated to other hospitals. Among them, 8,970 patients were returned back to the original hospital. Based on the results of this study, they could be used in could be used in planning a project which is aimed to reduce public health costs by investigating high-risk groups with particular injuries and preventing damage. In addition, the injury monitoring data which are continuously collected could be useful in monitoring and evaluating the efficiency of an injury prevention program.

• Korean Journal of Ecology and Environment
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• v.47 no.4
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• pp.239-246
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• 2014

To cope with the potential risks associated with invasive alien species, the Korean Government implemented the Biodiversity Act in 2014. Among the alien species not yet introduced into that country, 24 have been designated as alert species. These include mammals, birds, fish, mollusks, insects, and plants that may invade and cause serious harm to the ecosystem. Approval from the Ministry of Environment is required to import or carry any of them. Although these measures are more advanced than those from the previous legal framework, several terms still need to be improved. First, the category of alert species should cover not only those not yet introduced but also those that are being raised or cultivated at aquariums, botanical gardens, and zoos. Second, for applicants who intend to import or carry alert species, the government must provide them with detailed standards for the ecological risk assessment of alert species as well as guidelines for their safe use in Korea to prevent their unregulated release from confinement facilities into natural environments. Third, tools and protocols should be developed for early detection and rapid responses to those escapes.

• Journal of Life Science
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• v.25 no.11
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• pp.1324-1330
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• 2015

The present study was performed to analyze and review the physical and immune responses to overtraining syndrome in humans. Overreaching refers to the initial phage of overtraining syndrome and has been known as a physical fatigue which is mainly from metabolic imbalance. It has been known that overtraining also results in a loss of adaptability which may lead to an attenuation of exercise performance, sleeping disorder, central fatigue, neurohormonal changes, difficulty recovery to physical stress, and immunological changes. Additionally, overtraining syndrome is characterized by persistent fatigue, poor performance in sport due to the prolonged and strenuous physical training. Also, previous studies reported that endurance athletes experienced a high incidence of URTI during intense training and the post training. And also, high-performance athletes reported that suppression of cell mediated and anti-body mediated immune function. NK cell numbers were also reduced in the period of overtraining syndrome. Major components of prevention and treatment for the overtraining syndrome are screening, education, and detraining. Furthermore, the combination of these prevention and treatment strategies will be much helpful. Therefore, the current review will be helpful for athletes and individuals who are at the risk of overtraining syndrome.

• Journal of Preventive Medicine and Public Health
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• v.37 no.4
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• pp.381-389
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• 2004

Objective : To compare the efficacy between SKI306X and Diclofenac by using generalized estimating equations (GEE) methodology in the analysis of correlated bivariate binary outcome data in Osteoarthritis (OA) diseases. Methods : A randomized, double-blind, active comparator-controlled, non-inferiority clinical trial was conducted at 5 institutions in Korea with the random assignment of 248 patients aged 35 to 75 years old with OA of the knee and clinical evidence of OA. Patients were enrolled in this study if they had at least moderate pain in the affected knee joint and a score larger than 35mm as assessed by VAS (Visual Analog Scale). The main exposure variable was treatment (SKI 306X vs. Diclofenac) and other covariates were age, sex, BMI, baseline VAS, center, operation history (Yes/No), NSAIDS (Y/N), acupuncture (Y/N), herbal medicine (Y/N), past history of musculoskeletal disease (Y/N), and previous therapy related with OA (Y/N). The main study outcome was the change of VAS pain scores from baseline to the 2nd and 4th weeks after treatment. Pain scores were obtained as baseline, 2nd and 4th weeks after treatment. We applied GEE approach with empirical covariance matrix and independent(or exchangeable) working correlation matrix to evaluate the relation of several risk factors to the change of VAS pain scores with correlated binary bivariate outcomes. Results : While baseline VAS, age, and acupuncture variables had protective effects for reducing the OA pain, its treatment (Joins/Diclofenac) was not statistically significant through GEE methodology (ITT:aOR=1.37, 95% CI=(0.8200, 2.26), PP:aOR=1.47, 95% CI=(0.73, 2.95)). The goodness-of-fit statistic for GEE (6.55, p=0.68) was computed to assess the adequacy of the fitted final model. Conclusions : Both ANCOVA and GEE methods yielded non statistical significance in the evaluation of non-inferiority of the efficacy between SKI306X and Diclofenac. While VAS outcome for each visit was applied in GEE, only VAS outcome for the fourth visit was applied in ANCOVA. So the GEE methodology is more accurate for the analysis of correlated outcomes.

• Journal of Preventive Medicine and Public Health
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• v.48 no.2
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• pp.94-104
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• 2015

Objectives: Mental health disorders and suicide are an important and growing public health concern in Korea. Evidence has shown that both globally and in Korea, obesity is associated with an increased risk of developing some psychiatric disorders. Therefore, we examined the association between distorted body weight perception (BWP) and suicidal ideation. Methods: Data were obtained from the 2007-2012 Korea National Health and Nutritional Evaluation Survey (KNHANES), an annual cross-sectional nationwide survey that included 14 276 men and 19 428 women. Multiple logistic regression analyses were conducted to investigate the associations between nine BWP categories, which combined body image (BI) and body mass index (BMI) categories, and suicidal ideation. Moreover, the fitness of our models was verified using the Akaike information criterion. Results: Consistent with previous studies, suicidal ideation was associated with marital status, household income, education level, and perceived health status in both genders. Only women were significantly more likely to have distorted BWP; there was no relationship among men. In category B1 (low BMI and normal BI), women (odds ratio [OR], 2.25; 95% confidence interval [CI], 1.48 to 3.42) were more likely to express suicidal ideation than women in category B2 (normal BMI and normal BI) were. Women in overweight BWP category C2 (normal BMI and fat BI) also had an increased OR for suicidal ideation (OR, 2.25; 95% CI, 1.48 to 3.42). Those in normal BWP categories were not likely to have suicidal ideation. Among women in the underweight BWP categories, only the OR for those in category A2 (normal BMI and thin BI) was significant (OR, 1.34; 95% CI, 1.13 to 1.59). Conclusions: Distorted BWP should be considered an important factor in the prevention of suicide and for the improvement of mental health among Korean adults, especially Korean women with distorted BWPs.