• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.129-137
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• 2016

A colonoscopy is important because it detects the presence of polyps in the colon that can lead to colon cancer. How often one needs to repeat a colonoscopy may depend on various factors. The main purpose of this study is to determine personalized surveillance interval of colonoscopy based on characteristics of patients including their clinical information. The clustering analysis using a partitioning around medoids algorithm was conducted on 625 patients who had a medical examination at Korea University Anam Hospital and found several subgroups of patients. For each cluster, we then performed survival analysis that provides the probability of having polyps according to the number of days until next visit. The results of survival analysis indicated that different survival distributions exist among different patients' groups. We believe that the procedure proposed in this study can provide the patients with personalized medical information about how often they need to repeat a colonoscopy.

• Health Policy and Management
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• v.17 no.3
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• pp.50-67
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• 2007

This study investigated income elasticity of household health expenditures and differences by income level from 1998 through 2003. Data from Korean Labor and Income Panel Study was used for empirical analyses. To estimate the income effects on health expenditure, the two-part model was employed: a logistic regression for any health expenditure-first part-and a Ordinary Least Square regression for health expenditure conditional on any spending-second part. To estimate income elasticity, both health expenditure and income were log transformed in the second part. In addition, the random effects(RE) model was used for a longitudinal panel which was continuously followed from 1998 through 2003 to estimate income effects on health expenditures controlling for within and between unobservable household characteristics. Furthermore, difference in income effects on health expenditure across income level was investigated. Although income slightly increased odds of any health expenditure, there was not no table differences across income level. Income significantly increased health expenditures during study period(overall income elasticity: about 0.2) and the highest 20% income group presented higher income elasticity than the lowest 20% income group.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.151-151
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• 2019

Because drought is a complex and stochastic phenomenon in nature, statistical approaches for drought assessment receive great attention for water resource planning and management. Generally drought characteristics such as severity, duration and intensity are modelled separately. This study aims to develop a relationship between drought characteristics using a bivariate copula model. To achieve the objective, we calculated the Standardized Precipitation Index (SPI) using rainfall data at 6 rain gauge stations for the period of 1961-1999 in Jehlum River Basin, Pakistan, and investigated the drought characteristics. Since there is a significant correlation between drought severity and duration, they are usually modeled using different marginal distributions and joint distribution function. Using exponential distribution for drought severity and log-logistic distribution for drought duration, the Galambos copula was recognized as best copula to model joint distribution of drought severity and duration based on the KS-statistic. Various return periods of drought were calculated to identify time interval of repeated drought events. The result of this study can provide useful information for effective water resource management and shows superiority against univariate drought analysis.

• Knowledge Management Research
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• v.20 no.3
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• pp.119-137
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• 2019

With the recent spread of digital content, more people have been watching the digital content of TV programs on their PCs or mobile devices, rather than on TVs. With the change in such media use pattern, genres(types) of broadcast programs change in the flow of the times and viewers' trends. The programs that were broadcast on TVs have been released in digital content, and thereby people watching such content change their perception. For this reason, it is necessary to newly and differently classify genres(types) of broadcast programs on the basis of digital content, from the conventional classification of program genres(types) in broadcasting companies or relevant industries. Therefore, this study suggests a plan for newly classifying broadcast programs through using machine learning with the log data of people watching the programs in online media and for applying the new classification. This study is academically meaningful in the point that it analyzes and classifies program types on the basis of digital content. In addition, it is meaningful in the point that it makes use of the program classification algorithm developed in relevant industries, and especially suggests the strategy and plan for applying it.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.149-156
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• 2022

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

• Educational Technology International
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• v.18 no.2
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• pp.159-191
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• 2017

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on Learning Management System(LMS) help understanding instructors' pedagogical approach and the integration level of technologies. Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. As a case, we have clustered academic courses based on the usage levels and patterns of LMS in higher education using those three clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and suggest that academic leaders and university staff should look into the usage levels and patterns of LMS with more elaborated view and take an integrated approach with different analytic methods for their strategic decision on development of LMS.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.205-209
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• 2015

A significant amount of data including ongoing construction activities, work quantities, resources utilized by contractors, and site conditions is collected in highway construction sites on a daily basis by resident engineers. This data is commonly known as daily work reports (DWRs) in the U.S. Although a lot of time and effort is invested in collecting the DWR data, its utilization has been very limited. This paper discusses current practices of collecting and utilizing DWR data among various Departments of Transportation in the U.S., and discusses the challenges and opportunities for better collection and utilization of the data. An extensive literature review and two nationwide surveys in the U.S. were conducted as a part of this study. Finally, it provides a set of recommendations to effectively address the challenges identified and maximize the benefits of utilizing DWR data such as supporting various decisions for highway project development process. The findings of this study are implementable ideas that can aid DOTs in making data-driven decisions throughout the project development processes in the future.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.254-260
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• 2023

A sensor system has been developed that uses an ultrasonic sensor to detect the downhill slope on the side of a forest road and prevents a vehicle-type forest machine from rolling down a mountainside. A specular reflection of ultrasonic wave might cause severe issues in measuring distances to targets. By investigating the installation angle of the sensor to minimize the negative effects of specular reflection, the installation angle of lateral monitoring ultrasonic sensor could be determined based on the width of road shoulder. Obstacles such as small rocks or piece of log in a forest road may cause the forest machine to be overturned while the machine riding over due to excessive its posture change. It was determined that the laser sensor could be a part of a sensor system capable of specifying the location and size of small obstacles. Not only this sensor system including ultrasonic and laser sensors can issue a warning of dangerous sections to drivers in forest forwarders currently in use, but also it can be used as a driving safety sensor in autonomous forest machine or remote-control forest machine in the future.

• Korea Journal of Hospital Management
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• v.29 no.1
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• pp.19-31
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• 2024

Purposes: The purpose of this study is to analyze cases of development and operation of a mentoring program that provides psychological support to new employees and helps them adapt to work, thereby applying it to actual work and laying the foundation for follow-up research. Methodology: We explored the development and application process of A Hospital mentoring program by applying the mentoring program model developed according to the procedures of the ADDIE model, and confirmed the perceptions of participants who participated in the training course through analysis of activity logs and in-depth interviews. Findings: The main results of the case analysis are as follows. First, the curriculum was developed according to the stages of analysis, design, development, implementation, and evaluation. As a result of activity log and in-depth interview analysis, participants recognized that the mentoring program was helpful in forming social relationships, organizational adaptation, and preventing job turnover, and recognized difficulties in communication. Participants mentioned supplementing the operating system. Practical Implication: The results of a systematic review of the application and effectiveness of mentoring programs for new employees can serve as reference material for practical program design.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1601-1607
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• 2013

A determination of circulating tumor cell (CTC) effectiveness for prediction of progression-free survival (PFS) and overall survival (OS) was conducted as an adjunct to standard treatment of care in breast cancer management. Between November 2008 and March 2009, 22 metastatic and 12 early stage breast carcinoma patients, admitted to Ankara Oncology Training and Research Hospital, were included in this prospective trial. Patients' characteristics, treatment schedules and survival data were evaluated. CTC was detected twice by CellSearch method before and 9-12 weeks after the initiation of chemotherapy. A cut-off value equal or greater than 5 cells per 7.5 ml blood sample was considered positive. All patients were female. Median ages were 48.0 (range: 29-65) and 52.5 (range: 35-66) in early stage and metastatic subgroups, respectively. CTC was positive in 3 (13.6%) patients before chemotherapy and 6 (27.3%) patients during chemotherapy in the metastatic subgroup whereas positive in only one patient in the early stage subgroup before and during chemotherapy. The median follow-up was 22.0 (range: 21-23) and 19.0 (range: 5-23) months in the early stage and metastatic groups, respectively. In the metastatic group, both median PFS and OS were significantly shorter in any time CTC positive patients compared to CTC negative patients (PFS: 4.0 vs 14.0 months, Log-Rank p=0.013; and OS: 8.0 months vs. 20.5 months, Log-Rank p<0.001). OS was affected from multiple visceral metastatic sites (p=0.055) and higher grade (p=0.044) besides CTC positivity (log rank p<0.001). Radiological response of chemotherapy was also correlated with better survival (p<0.001). As a result, CTC positivity was confirmed as a prospective marker even in a small patient population, in this single center study. Measurement of CTC by CellSearch method in metastatic breast carcinoma cases may allow indications of early risk of relapse or death with even as few as two measurements during a chemotherapy program, but this finding should be confirmed with prospective trials in larger study populations.