• Tuberculosis and Respiratory Diseases
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• v.85 no.1
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• pp.74-79
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• 2022

Background: The effect of underlying chronic obstructive pulmonary disease (COPD) on coronavirus disease 2019 (COVID-19) during a pandemic is controversial. The purpose of this study was to examine the prognosis of COVID-19 according to the underlying COPD. Methods: COVID-19 patients were assessed using nationwide health insurance data. Comorbidities were evaluated using the modified Charlson Comorbidity Index (mCCI) which excluded COPD from conventional CCI scores. Baseline characteristics were assessed. Univariable and multiple logistic and linear regression analyses were performed to determine effects of variables on clinical outcomes. Ages, sex, mCCI, socioeconomic status, and underlying COPD were selected as variables. Results: COPD patients showed older age (71.3±11.6 years vs. 47.7±19.1 years, p<0.001), higher mCCI (2.6±1.9 vs. 0.8±1.3, p<0.001), and higher mortality (22.9% vs. 3.2%, p<0.001) than non-COPD patients. The intensive care unit admission rate and hospital length of stay were not significantly different between the two groups. All variables were associated with mortality in univariate analysis. However, underlying COPD was not associated with mortality unlike other variables in the adjusted analysis. Older age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.11-1.14; p<0.001), male sex (OR, 2.29; 95% CI, 1.67-3.12; p<0.001), higher mCCI (OR, 1.30; 95% CI, 1.20-1.41; p<0.001), and medical aid insurance (OR, 1.55; 95% CI, 1.03-2.32; p=0.035) were associated with mortality. Conclusion: Underlying COPD is not associated with a poor prognosis of COVID-19.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.85-100
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• 2022

Purpose: This study is aimed to examine the association between initial enteral nutrition (EN) and the clinical prognosis among patients with severe and multiple traumatic injuries, and to investigate whether this association is modified by the patients' catabolic status. Methods: This was a retrospective study of 302 adult patients with severe and multiple traumatic injuries admitted between January 2017 and September 2020 at Ajou University hospital in Suwon, Korea. The initial nutritional support by EN and parenteral nutrition were monitored up to day 7 after admission. Patients were classified into "No", "Low", and "High" EN groups according to the level of initial EN. Multivariable-adjusted logistic regression and linear regression models were used to estimate the association of the initial EN levels at hospital admission with the risk of mortality, morbidities, and levels of nutrition-associated biochemical markers. Results: High EN support was associated with reduced mortality (odds ratio, 0.07; 95% confidence interval [CI], 0.02, 0.32) and lower levels of C-reactive protein (β, -0.22; 95% CI, -8.66, 1.48), but longer stay in the intensive care unit (β, 0.19; 95% CI, 1.82, 11.32). In analyses stratified by catabolic status, there were fewer incidences of hospital-acquired infections with increasing EN levels in the moderate or higher nitrogen balance group than in the mild nitrogen balance group. Conclusion: Our observation of the inverse association between levels of initial EN administration with mortality risk and inflammatory markers may indicate the possible benefits of active EN administration to the recovery process of severe and multiple trauma patients. Further studies are warranted on whether the catabolic status modifies the association between the initial EN and prognosis.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.31-41
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• 2024

Objective : Collateral circulation is associated with the differential treatment effect of endovascular thrombectomy (EVT) in acute ischemic stroke. We aimed to verify the ability of the collateral map to predict futile EVT in patients with acute anterior circulation ischemic stroke. Methods : This secondary analysis of a prospective observational study included data from participants underwent EVT for acute ischemic stroke due to occlusion of the internal carotid artery and/or the middle cerebral artery within 8 hours of symptom onset. Multiple logistic regression analyses were conducted to identify independent predictors of futile recanalization (modified Rankin scale score at 90 days of 4-6 despite of successful reperfusion). Results : In a total of 214 participants, older age (odds ratio [OR], 2.40; 95% confidence interval [CI], 1.56 to 3.67; p<0.001), higher baseline National Institutes of Health Stroke Scale (NIHSS) scores (OR, 1.12; 95% CI, 1.04 to 1.21; p=0.004), very poor collateral perfusion grade (OR, 35.09; 95% CI, 3.50 to 351.33; p=0.002), longer door-to-puncture time (OR, 1.08; 95% CI, 1.02 to 1.14; p=0.009), and failed reperfusion (OR, 3.73; 95% CI, 1.30 to 10.76; p=0.015) were associated with unfavorable functional outcomes. In 184 participants who achieved successful reperfusion, older age (OR, 2.30; 95% CI, 1.44 to 3.67; p<0.001), higher baseline NIHSS scores (OR, 1.12; 95% CI, 1.03 to 1.22; p=0.006), very poor collateral perfusion grade (OR, 4.96; 95% CI, 1.42 to 17.37; p=0.012), and longer door-to-reperfusion time (OR, 1.09; 95% CI, 1.03 to 1.15; p=0.003) were associated with unfavorable functional outcomes. Conclusion : The assessment of collateral perfusion status using the collateral map can predict futile EVT, which may help select ineligible patients for EVT, thereby potentially reducing the rate of futile EVT.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.1
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• pp.66-74
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• 2005

This survey was conducted to assess the consumer perceptions of food-related hazard in 500 housewives from all over Korea. The subjects were selected by stratified random sampling method. The survey was performed using structured questionnaire through telephone interview by skilled interviewers. The results showed that 34.6% of the respondents felt secure and were not concerned about food safety, and 65.4% were concerned about food safety. Logistic regression analysis showed that the increasing concern on food brands, food additives (such as food preservatives and artificial color), and imported foodstuffs indicated the current increasing concern on food safety. Other related factors indicating the increasing concern on food safety were education level and care for children's health. The respondents who cared about food safety expressed a high degree of concern on processed foodstuffs such as commercial boxed lunch (93.3%), imported foods (92.7%), fastfoods (89.9%), processed meat products (88.7%), dining out (85.6%), cannery and frozen foods (83.5%), and instant foods (82.0%). The lowest degree of concern was on rice. All the respondents perceived that residues of chemical substances such as pesticides and food additives, and endocrine disrupters were the most potential food risk factors, followed by food-borne pathogens, and GMOs (Genetically Modified Organisms). However, these results were not consistent with scientific judgment. Therefore, more education and information were needed for consumers' awareness of facts and myths about food safety. In addition, the results showed that consumers put lower trust in food products information such as food labels, cultivation methods (organic or not), quality labels, and the place of origin. Nevertheless, the respondents expressed their desire to overcome alienation, and recognized the importance of knowing of the origin or the producers of food. They identified that people who need to take extreme precautions on food contamination were the producers, government officials, food companies, consumers, the consumer's association, and marketers, arranged in the order of highest to lowest. They also believed that the production stage of agriculture was the most important step for improving the level of food safety Therefore, the results indicated that there is a need to introduce safety systems in the production of agricultural products, as follows: Good Agricultural Practice (GAP), Hazard Analysis and Critical Control Point (HACCP), and Traceability System (75).

• Journal of radiological science and technology
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• v.31 no.3
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• pp.239-247
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• 2008

In order to study the occurrence of symptoms of musculoskeletal disorders of radio-technologists employed at metropolitan general hospitals and the factors that influence such occurrence, standardized questionnaire by NIOSH that was modified and supplemented to be suitable for conditions in Korea was used. Answers collected from 143 radio-technologists in two weeks from June 13, 2007 were analyzed and the results are as follows. Factor that influence symptoms of musculoskeletal disorders by area were analyzed through multiple logistic regression analysis and the results found that in the neck area, risk increased as the burdening work category 2(Korea ministry of labor)(OR=3.94) and burdening work category 9(Korea ministry of labor)(OR=4.72) increased. In the shoulder region, risk increased as burdening work category 2(Korea ministry of labor)(OR=5.36), burdening work category 7(Korea ministry of labor)(OR=3.90), and burdening work category 9 (Korea ministry of labor)(OR=5.76) increased. In the arm/hand/wrist regions, risk increased as burdening work category 2 (Korea ministry of labor) (OR=6.91), and burdening work category 9 (Korea ministry of labor)(OR=3.76) increased. In the lower back region, risk increased as burdening work category 2 (Korea ministry of labor) (OR=3.06), and burdening work category 8 (Korea ministry of labor)(OR=8.14) increased. In the leg/knees/foot regions, risk increased as burdening work category 2 (Korea ministry of labor) (OR=3.63), and burdening work category 9 (Korea ministry of labor)(OR=2.96) increased. Conclusively, in factors that influence musculoskeletal disorder symptoms in radio-technologists, influence of subjective health conditions, total work experience, experience in current division, and burdening work category 2, 7, 8, and 9 (Korea ministry of labor) were most significant. Therefore, for preventive management, in addition to ergonomic and educational intervention for correcting improper posture during work, efforts for break time adjustment and stress reduction is needed, and encouragement and support for regular exercise is needed.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.3 no.1
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• pp.17-22
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• 2000

Purpose: This study was conducted to evaluate the association between H. pylori infection and socioeconomic status and to determine the current prevalence of H. pylori infection in Korean adolescents. Methods: A structured questionnaire was sent to the children's parents to obtain demographic information on the parents and environmental information. Of the 532 questionnaires sent out, 375 (70.5%; 170 girls and 205 boys) were returned. Their ages ranged from 10 to 15 years (mean, 12.9 years). After collecting blood samples, we measured serum IgG antibody to H. pylori using ELISA method. The association of risk factors such as age, sex, socioeconomic class, type of house, and crowding index with H. pylori infection were analyzed by multiple regression analysis. Socioeconomic status was estimated from the parents' education and occupation using a modified Hollingshead index. Results: The prevalence rate of H. pylori infection was 16.8% (63/375). It increased with age (10.3% at 10~11 years, 15.9% at 12~13 years, and 20.7% at 14~15 years). The H. pylori infection was inversely related to the socioeconomic class (6.3% for the upper class, 16.0% for the middle class, and 20.0% for the lower calss). Crowding condition and type of house did not affect significantly on seroprevalence of H. pylori infection. After logistic regression, we found that the odds ratio for age was 2.2 (95% confidence interval 0.9~5.4), and for socioeconomic status, 3.6 (95% confidence interval 0.5~28.9). Conclusion: The prevalence of H. pylori infection in Korean adolescents was 16.8%. It related inversely to socioeconomic status but was not statistically significant. Socioeconomic status based on parents' education and occupation seemed to affect more on H. pylori seroprevalence than crowding or type of house did.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.49-95
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• 2024

Polygraph examination, statement validity analysis and P300-based concealed information test are major three examination tools, which are use to determine a person's truthfulness and credibility in criminal procedure. Although polygraph examination is most common in criminal procedure, but it has little admissibility of evidence due to the weakness of scientific basis. In 1990s to support the weakness of scientific basis about polygraph, Farwell and Donchin proposed the P300-based concealed information test technique. The P300-based concealed information test has two strong points. First, the P300-based concealed information test is easy to conduct with polygraph. Second, the P300-based concealed information test has plentiful scientific basis. Nevertheless, the utilization of P300-based concealed information test is infrequent, because of the quantity of probe stimulus. The probe stimulus contains closed information that is relevant to the crime or other investigated situation. In tradition P300-based concealed information test protocol, three or more probe stimuli are necessarily needed. But it is hard to acquire three or more probe stimuli, because most of the crime relevant information is opened in investigative situation. In addition, P300-based concealed information test uses oddball paradigm, and oddball paradigm makes imbalance between the number of probe and irrelevant stimulus. Thus, there is a possibility that the unbalanced number of probe and irrelevant stimulus caused systematic underestimation of P300 amplitude of irrelevant stimuli. To overcome the these two limitation of P300-based concealed information test, one-probe P300-based concealed information test protocol is explored with various machine learning algorithms. According to this study, parameters of the modified one-probe protocol are as follows. In the condition of female and male face stimuli, the duration of stimuli are encouraged 400ms, the repetition of stimuli are encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. In the condition of two-syllable word stimulus, the duration of stimulus is encouraged 300ms, the repetition of stimulus is encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. It was also conformed that the logistic regression (LR), linear discriminant analysis (LDA), K Neighbors (KNN) algorithms were probable methods for analysis of P300 amplitude. The one-probe P300-based concealed information test with machine learning protocol is helpful to increase utilization of P300-based concealed information test, and supports to determine a person's truthfulness and credibility with the polygraph examination in criminal procedure.