Youngjin Choi's research while affiliated with Eulji University and other places

Background Injuries caused by RTA are classified under the International Classification of Diseases-10 as ‘S00-T99’ and represent imbalanced samples with a mortality rate of only 1.2% among all RTA victims. To predict the characteristics of external causes of road traffic accident (RTA) injuries and mortality, we compared performances based on differences in the correction and classification techniques for imbalanced samples. Methods The present study extracted and utilized data spanning over a 5-year period (2013–2017) from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS), a national level survey conducted by the Korea Disease Control and Prevention Agency, A total of eight variables were used in the prediction, including patient, accident, and injury/disease characteristics. As the data was imbalanced, a sample consisting of only severe injuries was constructed and compared against the total sample. Considering the characteristics of the samples, preprocessing was performed in the study. The samples were standardized first, considering that they contained many variables with different units. Among the ensemble techniques for classification, the present study utilized Random Forest, Extra-Trees, and XGBoost. Four different over- and under-sampling techniques were used to compare the performance of algorithms using “accuracy”, “precision”, “recall”, “F1”, and “MCC”. Results The results showed that among the prediction techniques, XGBoost had the best performance. While the synthetic minority oversampling technique (SMOTE), a type of over-sampling, also demonstrated a certain level of performance, under-sampling was the most superior. Overall, prediction by the XGBoost model with samples using SMOTE produced the best results. Conclusion This study presented the results of an empirical comparison of the validity of sampling techniques and classification algorithms that affect the accuracy of imbalanced samples by combining two techniques. The findings could be used as reference data in classification analyses of imbalanced data in the medical field.

In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency’s (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses. Due to the number of variables and differing units, the rates of survival and mortality related to road traffic accidents were imbalanced, so the data was corrected and standardized before the classification models’ performances were compared. Using the importance analysis, the main diagnosis, the type of injury, the site of the injury, the type of injury, the operation status, the type of accident, the role at the time of the accident, and the sex were selected as the analysis factors. The biggest contributing factor was the role in the accident, which is the driver, and the major sites of the injuries were head injuries and deep injuries. Using selected factors, comparisons of the classification performance of each model indicated RBF-SVM and RF models were superior to the others. Of the SVM models, the RBF kernel model was superior to the linear kernel model; it can be inferred that the performance of the high-dimensional transformed RBF model is superior when the dimension is complex because of the use of multiple variables. The findings suggest there are limitations to analyses involving imbalanced, multidimensional original data, such as data on road traffic mortality. Thus, analyses must be performed after imbalances are corrected.

Recently IT infrastructures change to cloud computing, the demand of cloud data center increased. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared computing resources that can be rapidly provisioned and released with minimal management effort, the interest on data centers to provide the cloud computing services economically and variably is increasing. This study analyzes the factors to improve the power efficiency while securing scalability of data centers and presents the considerations for cloud data center construction in terms of power distribution method, power density per rack and expansion unit separately. The result of this study may be used for making rational decisions concerning the power input, voltage transformation and unit of expansion when constructing a cloud data center or migrating an existing data center to a cloud data center.

Accuracy and consistency in morbidity coding are important in both clinical research and practice. However,Health Information Managers (HIMs) sometimes face difficulties in assigning morbidity codes. To assist them,the Korean Medical Record Association operates an online coding clinic bulletin board, on which HIMs can post questions and receive answers. Frequency analysis and Fisher's exact testing were performed to identify differences among the types of questions posted and the characteristics of the HIMs who posted them. Through statistical analysis, it was found that HIMs working at hospitals with fewer than 500 beds and those with more than 10 years of work experience were found to post more questions than other HIMs. The study also identified the characteristics of HIMs who require more coding education and particular diagnoses for which further training is required. Our findings will assist the development of coding procedures, guidelines, education programs, and a more user-friendly database.

Accuracy and consistency in morbidity coding are important in both clinical research and practice. However, Health Information Managers (HIMs) sometimes face difficulties in assigning morbidity codes. To assist them, the Korean Medical Record Association operates an online coding clinic bulletin board, on which HIMs can post questions and receive answers. Frequency analysis and Fisher's exact testing were performed to identify differences among the types of questions posted and the characteristics of the HIMs who posted them. Through statistical analysis, it was found that HIMs working at hospitals with fewer than 500 beds and those with more than 10 years of work experience were found to post more questions than other HIMs. The study also identified the characteristics of HIMs who require more coding education and particular diagnoses for which further training is required. Our findings will assist the development of coding procedures, guidelines, education programs, and a more user-friendly database.

The various development frameworks cause problems such as the cost of the system maintenance, the outsourcing firm dependency and the lack of interoperability between systems. In order to solve these problems, the Korean government has developed a standard development framework for e-Government, is called the eGovFrame using open source. And many agencies used eGovFrame, HIRA also used standard development framework. In this study we showed that applied to the practices for DUR(Drug Utilization Review) system of Korea HIRA.