• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5633-5636
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• 2012

Purpose: Breast cancer is an important cause of cancer-related death in women. Numerous studies have evaluated the association between the insertion/deletion (I/D) polymorphism in the angiotensin-converting enzyme (ACE) gene and breast cancer risk. However, the specific association is still controversial rather than conclusive. Therefore, we performed a meta-analysis of related studies to address this controversy. Methods: PubMed, EMBASE, Google Scholar and the Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between the I/D polymorphism in the ACE gene and susceptibility to breast cancer. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. Results: 10 separate studies of 7 included articles with 10,888 subjects on the relation between the I/D polymorphism in the ACE gene and breast cancer were analyzed by meta-analysis, and our results showed no association between the I/D polymorphism in the ACE gene and breast cancer in total population and different populations. No publication bias was found in the present study. Conclusions: The ACE I/D polymorphism may not be associated with breast cancer risk. Further large and well-designed studies are needed to confirm this conclusion.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4403-4407
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• 2012

Background: Glutathione-S-Transferase T1 (GSTT1) gene has been shown to be involved in the development of esophageal cancer. However, the results have been inconsistent. In this study, the authors performed a meta-analysis to clarify the association between GSTT1 polymorphism and esophageal cancer risk among Chinese Han population. Methods: Published literature from PubMed, the China National Knowledge Infrastructure and Wanfang Data were searched. Pooled odds ratio (OR) and 95% confidence interval (95%CI) was calculated using a fixed- or random-effects model. Results: Eleven studies with a total of 2779 individuals were included in the meta-analysis. The results showed that GSTT1 null genotype was significantly associated with esophageal cancer risk in Chinese (OR = 1.31, 95%CI 1.12 to 1.53, p = 0.001). Further sensitivity analyses confirmed the significant association. The cumulative meta-analysis showed a trend of an obvious association between GSTT1 null genotype and esophageal cancer risk as information accumulated by year. Conclusions: This meta-analysis suggests a significant association of GSTT1 null genotype with esophageal cancer risk in the Chinese Han population.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8083-8087
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• 2014

Purpose: To investigate the association of IL-17F rs763780T>C with cancer risk. Materials and Methods: We searched the Cochrane Central Library, PubMed, MEDLINE, EMBASE, CNKI (China National Knowledge Infrastructure) and WangFang databases until May 2014 for a meta-analysis conducted using RevMan 5.2 software. Results: A total of ten papers were included into this meta analysis, involving 3, 336 cases and 4, 217 healthy people. There were no significant differences on association of IL-17F rs763780T>C polymorphism with cancer risk except in the CC vs TT genetic model. Although the the risk in the gastric cancer group is higher than that in control group, there were no significant differences on the association of IL-17F rs763780T>C polymorphism with other cancers. Conclusions: Our meta analysis reveal the IL-17A rs763780T>C gene polymorphism is involved in risk of gastric cancer but not other tumor types.

• Journal of Preventive Medicine and Public Health
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• v.54 no.2
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• pp.96-102
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• 2021

Objectives: Sarcopenia is a common disease in the elderly population that causes disability, poor quality of life, and a high risk of death. In the current study, we conducted a meta-analysis to report basic knowledge about the prevalence of sarcopenia in the elderly in Korea. Methods: We searched for articles in the MEDLINE, Cochrane Library, Embase, and Scopus databases published until December 28, 2020. Studies investigating the prevalence of sarcopenia in elderly Koreans aged ≥65 years were included. The methodological quality of the studies was evaluated using the Newcastle-Ottawa scale. Publication bias was evaluated using the Egger test and funnel plots. Results: In total, 3 studies and 2922 patients were included in the meta-analysis. All 3 studies used the European Working Group on Sarcopenia in Older People criteria for the diagnosis of sarcopenia. The total prevalence of sarcopenia was 13.1-14.9% in elderly men and 11.4% in elderly women. Conclusions: This meta-analysis is the first to estimate the pooled prevalence of sarcopenia in elderly Koreans, and its findings suggest that sarcopenia is common in this population. Therefore, attention should be paid to the prevention and control of sarcopenia.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Asian Oncology Nursing
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• v.12 no.4
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• pp.257-266
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• 2012

Purpose: The purpose of this study was to evaluate the effectiveness of patient education interventions on distress, self-care knowledge and self-care behavior in cancer patients. Methods: A total of 1,102 studies were retrieved from 6 electronic databases in Korea. From these studies, 18 studies met the inclusion criteria with a total of 850 participants. Two authors independently assessed the methodological quality by Cochrane's Risk of Bias and Methodological Items for Non Randomized Studies. The data were analyzed by the RevMan 5.1 program of Cochrane library. Results: Overall effect size of education interventions on anxiety was -2.12 (95% CI:-3.90, -0.34) (p<.001). The effects on self-care knowledge and self care behavior were -1.08 (95% CI:-1.73, -0.43) (p=.001), and -1.41 (95% CI:-2.13, -0.68) (p<.001), respectively. Publication bias was detected as evaluated by funnel plot, but the fail-safe number was moderate. Conclusion: This study suggests that patient education interventions can relieve anxiety and self-care. Further randomized controlled trials studies are needed to evaluate the effects of patient education intervention on depression.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.441-446
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• 2001

On global interconnectivity, the activation of real-time and worldwide contents will permeate and impact all aspects of day-to-day life well throughout this century. In managing contents and models, we too will see the impact of this rapidly changing environment. The real time availability of contents pertaining to a companys supply chain through means of the Internet and mobile networks(e.g., the IMT-2000) will necessitate a change in decision-making processes for effective management of contents and models. To increase the availability of many contents and models, a management system should have adaptive function in proving adequate content and model for companies. In the respect of management of contents and models, this paper discusses a production mechanism of meta-knowledge for effectively managing contents and models. Through two experimental analyses with the production mechanism, it is proven that the system enabling adaptive contents and models provision goes beyond existing ones in view of efficiency of management of contents and models in the wire and wireless networks.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.163-176
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• 2024

The aim was to evaluate the effects of simulation based learning in psychiatry on self-efficacy, problem solving ability, and knowledge of nursing students. PubMed, Cochrane Library, Embase, CINAHL, KISS, RISS, and ScienceOn were searched until July 2023. A systematic review and meta-analysis was conducted of 22 studies (20 reports), with a total of 1,414 nursing students. Overall, simulation based learning in psychiatry appeared to have beneficial effects on self-efficacy (ES = 0.65, p < 0.001, I²=71%), problem solving ability (ES = 0.15, p < 0.001, I²=27%), and knowledge (ES = 0.45, p = 0.003, I²=84%). These results demonstrate that, if integrated appropriately, a simulation educational approach can be used as an active learning methodology in psychiatric academic settings.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.243-250
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• 2020

Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.3
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• pp.197-211
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• 2024

Purpose: This study aims to comprehensively assess the characteristics and effectiveness of immersive virtual reality (VR) or augmented reality (AR) in nursing education among nursing students and nurses. Methods: A thorough search was conducted in seven databases (PubMed, Embase, Cochrane Library, CINAHL, RISS, KMbase, and KoreaMed) for randomized controlled trials (RCTs) published in English or Korean before February 20, 2024. The quality of the included RCTs was assessed using the revised Cochrane Risk of Bias tool for randomized trials. A random-effects model was applied for the meta-analysis using Review Manager 5.4. Results: Out of the 15,840 studies extracted, ten were selected. Of those ten, the majority (six, 60%) were conducted on education dealing with specific nursing situations. In addition to the use of immersive VR or AR during nursing education, lectures, debriefing, and discussion processes were applied together, and device usage orientation was also provided. The meta-analyses showed that immersive VR or AR in nursing education significantly improved knowledge (standardized mean difference, SMD=2.64; 95% confidence interval, 95% CI=1.10~4.17) and skills (SMD=0.58, 95% CI=0.02~1.15). Conclusion: Immersive VR or AR in nursing education can effectively enhance knowledge and skills. However, for their development and implementation, various factors should be considered, and these findings are expected to provide valuable evidence regarding that concern.