• Journal of Distribution Science
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• v.12 no.6
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• pp.67-79
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• 2014

Purpose - This study aims to accomplish three major research goals. First, it strives to change consumers' focus from peripheral routes to a central route of public service advertising related to the good health policy, without problematic effects, by influencing consumers' knowledge or involvement. Second, this study examines the elaboration likelihood model (ELM) and construal level theory (CLT). Specifically, we consider that the central route of ELM might correspond with the focal goal of CLT. Third, this study analyzes ELM through CLT. That is, ELM predicted that low involvement would take the peripheral route, and high involvement would take the central route. Research design, data, and methodology - This study consisted of three experiments. The first experiment had a 2×2 between-subject design. The subjects were university students and the research period was approximately one year. The first independent variable was the involvement of the overweight issue; this variable was measured and split by the median. The second independent variable was the temporal distance (near vs. distant future); this variable was manipulated. The second experiment also had a 2×2 between-subject design. The first variable was the involvement of cervical adenocarcinoma prevention, and was considered already manipulated by sex. Specifically, males had a low involvement of the disease, but females had high involvement. The second independent variable was priming (power vs. submissive). Power priming would induce abstract thinking, but submissive priming would take concrete processing. The third experiment had a 2×2×2 between-subject design. The first variable was cognitive depletion, and was manipulated by memorizing 9-digit numbers. The second and third independent variables were involvement and abstract thinking induction, such as prior experiments. Data were collected through questionnaires, and were analyzed by an SPSS program. Major hypotheses were tested by examining the interaction effects through ANOVA. Results - Major findings are as follows. First, even for low-involved consumers in the overweight category, distant future manipulation induced them to focus not on the peripheral route but on the central route of the public service advertisement. This result does not correspond to the typical ELM prediction. Second, under power priming, low-involved males of the cervical adenocarcinoma category focused on the peripheral route because of the induction to abstract thinking. This result replicated the first experiment, and confirmed the theoretical robustness. Third, high-involved females focused not on the central but on the peripheral route under the mixed condition of cognitive depletion and near future manipulation. Depletion consumed cognitive resources, and the processing mode of consumers changed from systematic to heuristic. Conclusions - ELM needs to be complemented through CLT in context of public service good health advertising. Specifically, the involvement of ELM may impact consumers' thinking mode (abstract vs. concrete), and the interaction effects may influence consumers' focus on advertising (central vs. peripheral route). This study's limitations were bounded subjects, limited stimuli, and somewhat weak external validity.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.435-444
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• 2019

The purpose of this study was to investigate the relationship between sleep duration and muscle strength in Korean adults aged 19 years and older. The cross-sectional analysis was conducted using the 2016 National Health and Nutrition Examination Survey data and Chi square test and multiple regression analysis were used. As a result of the analysis, the grip strength of those with more than weekday average sleep duration of 9 hours was found to be -1.267kg compared with those with weekday average sleep duration of 7 hours. The grip strength of those with more than weekend average sleep duration of 9 hours was found to be -0.879kg compared with those with weekend average sleep duration of 7 hours. In model simultaneously adjusting for both the average weekday and weekend average sleep duration, weekday average sleep duration of 9 hours was found to be -1.034kg compared with those with weekday average sleep duration of 7 hours. Therefore, careful observation will be required in light of the fact that both sleep duration and grip strength can predict future health conditions.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• The Journal of Bigdata
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• v.6 no.1
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• pp.51-62
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• 2021

In the field of medical artificial intelligence, there have been a lot of researches on disease prediction and classification algorithms that can help doctors judge, but relatively less interested in artificial intelligence that can help medical consumers acquire and judge information. The fact that more than 150,000 questions have been asked about which hospital to go over the past year in NAVER portal will be a testament to the need to provide medical information suitable for medical consumers. Therefore, in this study, we wanted to establish a classification model that classifies 8 medical subjects for symptom text directly described by patients which was collected from NAVER portal to help consumers choose appropriate medical subjects for their symptoms. In order to ensure the validity of the data involving patients' subject matter, we conducted similarity measurements between objective symptom text (typical symptoms by medical subjects organized by the Seoul Emergency Medical Information Center) and subjective symptoms (NAVER data). Similarity measurements demonstrated that if the two texts were symptoms of the same medical subject, they had relatively higher similarity than symptomatic texts from different medical subjects. Following the above procedure, the classification model was constructed using a ridge regression model for subjective symptom text that obtained validity, resulting in an accuracy of 0.73.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.331-343
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• 2019

The purpose of this study is to observe the trends of heart age of Koreans by using the predictor of heart age of the Framingham Heart Study. The subjects were 20,012 adults aged 30~74 years who were enrolled in the Korean National Health and Nutrition Examination Survey from 2005~2013. They filled in the determinants data and they had no history of cardiovascular disease (CVD). The heart age was calculated using a non-laboratory based model of prediction. The difference of heart age and chronological age, and the rate of excessive heart age over 10 years were calculated. The annual trend, the difference according to gender, the age bracket and geographic region, the heart age were all evaluated. Data analysis performed using the SAS program (version 9.3). Complex designed analysis was done. The heart age showed differences according to gender, age bracket and geographic region. The heart age is a useful comprehensive indicator for predicting the CVD events in the near future. So, it could be used for the purposes of exercising caution and guidance on CVD for administering medical care. It is strongly recommended to use heart age as an indicator for customized medical management to focus efforts on relatively vulnerable subjects and their factors for CVD. Further study on Koreans' customized heart age is needed.

• Journal of Gastric Cancer
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• v.23 no.2
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• pp.355-364
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• 2023

Background: There are no clear guidelines to determine whether to perform D1 or D1+ lymph node dissection in early gastric cancer (EGC). This study aimed to develop a nomogram for estimating the risk of extraperigastric lymph node metastasis (LNM). Materials and Methods: Between 2009 and 2019, a total of 4,482 patients with pathologically confirmed T1 disease at 6 affiliated hospitals were included in this study. The basic clinicopathological characteristics of the positive and negative extraperigastric LNM groups were compared. The possible risk factors were evaluated using univariate and multivariate analyses. Based on these results, a risk prediction model was developed. A nomogram predicting extraperigastric LNM was used for internal validation. Results: Multivariate analyses showed that tumor size (cut-off value 3.0 cm, odds ratio [OR]=1.886, P=0.030), tumor depth (OR=1.853 for tumors with sm2 and sm3 invasion, P=0.010), cross-sectional location (OR=0.490 for tumors located on the greater curvature, P=0.0303), differentiation (OR=0.584 for differentiated tumors, P=0.0070), and lymphovascular invasion (OR=11.125, P<0.001) are possible risk factors for extraperigastric LNM. An equation for estimating the risk of extraperigastric LNM was derived from these risk factors. The equation was internally validated by comparing the actual metastatic rate with the predicted rate, which showed good agreement. Conclusions: A nomogram for estimating the risk of extraperigastric LNM in EGC was successfully developed. Although there are some limitations to applying this model because it was developed based on pathological data, it can be optimally adapted for patients who require curative gastrectomy after endoscopic submucosal dissection.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.324-336
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• 2020

Cardiovascular calcifications can occur in various cardiovascular diseases and can serve as a biomarker for cardiovascular event prediction. Advances in CT have enabled evaluation of calcifications in cardiovascular structures not only on ECG-gated CT but also on non-ECG-gated CT. Therefore, many studies have been conducted on the clinical relevance of cardiovascular calcifications in patients. In this study, we divided cardiovascular calcifications into three classes, i.e., coronary artery, thoracic aorta, and cardiac valve calcifications, which are closely associated with cardiovascular events. Further, we briefly described pericardial calcifications, which can be found incidentally. Since the start of lung cancer screening in Korea in the second half of 2019, the number of non-enhanced, non-ECG-gated, low-dose chest CT has been increasing, and the number of incidentally found cardiovascular calcifications has also been increasing. Therefore, understanding the relevance of cardiovascular calcifications on non-enhanced, non-ECG-gated, low-dose chest CT and their proper reporting are important for radiologists.