• The Korean Journal of Archival Studies
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• no.61
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• pp.157-190
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• 2019

The Records Appraisal Review contains the processes and results of appraisal works for records. This study is an analysis of the 2018 Records Appraisal Review of 22 Local Education Offices where records managers were deployed. To ensure reliability of analyzing results, the analysis data preparation criteria were applied to maintain consistency in the analysis data preparation. Also, statistics by appraisal process and retention period were cross-checked. Through the analysis of the Records Appraisal Review identifies the actual condition for records appraisal. Based on this, the improvement measures that are applicable in practice were proposed in terms of records management of department, appraisal works of records manager and operation of Records Appraisal Review Commission.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.75-86
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• 2020

Hotel review data can extract various information that includes purchasing factors that lead to consumption, advantages, and disadvantages for hotels. In particular, the sentiment keyword of the review data helps consumers understand the pros and cons of hotels. However, it is not efficient for consumers to read a large number of reviews. Therefore, it is necessary to offer a summary review to customers. In this study, we suggest providing summary information on sentiment keywords association as well as a network of hotels based on sentiment keywords. Based on a sentiment keyword dictionary, the extracted sentiment keywords associations construct the hotel network through topological data analysis based mapper. This hotel network allows a consumer to find some hotels associated with specific sentiment keywords as well as recommends the same related hotels. This summary information provides users with a summarized emotional assessment of hotels and helps hotel marketing teams understand consumers' perceptions of their hotel.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of Society of Preventive Korean Medicine
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• v.19 no.2
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• pp.51-56
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• 2015

Objective : We analyzed present condition of cooperative medical care using claims data of HEALTH INSURANCE REVIEW & ASSESSMENT SERVICE form patients treated by Korean-Western cooperative medicine. The study aimed to offer guidelines in selecting disease-related research in developing Korean-Western convergence technology. Method : Based on the patients using Korean medical service, we analyzed claims data of patients using Korean medical service and western medical service from January 2012 to December 2013. We were assigned to the server remotely. With the concept of 'episode of care', we rebuilt claims data and analyzed present condition of cooperative medical usage. Results : We analyzed present condition of cooperative medical care per episode of care. Among outpatients, Low back pain, lumbar region(M5456) was the highest number. Among inpatients, Sciatica due to intervertebral disc disorder(M511) was the highest number. Conclusion : Based on the claims data provided by HEALTH INSURANCE REVIEW & ASSESSMENT SERVICE, we have derived a list of multy frequently disease frequently treated by cooperative medical care by analyzing present condition.

• Review of Korea Contents Association
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• v.17 no.2
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• pp.29-33
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• 2019

• Health Policy and Management
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• v.16 no.2
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• pp.49-76
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• 2006

The aim of this study was to explore the effects of a computerized review program which was introduced in August 1, 2003, using claims data for acute respiratory infection related diseases. National Health Insurance (NHI) claims data on respiratory infection related diseases before and after the introduction, with six month intervals respectively, were used for the analysis. Clinic was the unit of observation, and clinics with only one physician whose specialty was internal medicine, pediatrics, otorhinolaryngology and family medicine and clinics with a general practitioner were selected. The final sample had 7,637 clinics in total. Indices used to measure practice pattern was prescription rates of antibiotics, prescription rates of injection drug per visit, treatment costs per claim, and total costs per claim. Changes in the number of claims for major disease categories and upcoding index for disease categories were used to measure claiming behavior. Data were analysed using descriptive analysis, t-test for indices changes before and after the introduction, analysis of variance (ANOVA) for practice pattern change for major disease categories, and multiple regression analysis to identify whether new system influenced on provider' practice patterns or not. Prescription of antibiotics, prescription rates of injection drug, treatment costs per claim, and total costs per claim decreased significantly. Results from multiple regression analysis showed that a computerized review system had effects on all the indices measuring behavior. Introduction of the new system had the spillover effects on the provider's behavior in the related disease categories in addition to the effects in the target diseases, but the magnitude of the effects were bigger among the target diseases. Rates of claims for computerized review over total claims for respiratory diseases significantly decreased after the introduction of a computerized review system and rates of claims for non target diseases increased, which was also statistically significant. Distribution of the number of claims by disease categories after the introduction of a computerized review system changed so as to increase the costs per claims. Analysis of upcoding index showed index for 'other acute lower respiratory infection (J20-22)', which was included in the review target, decreased and 'otitis media (H65, H66)', which was not included in the review target, increase. Factors affecting provider's practice patterns should be taken into consideration when policies on claims review method and behavior changes. It is critical to include strategies to decrease the variations among providers.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• Child Health Nursing Research
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• v.26 no.2
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• pp.309-322
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• 2020

Purpose: The purpose of this integrative review was to synthesize previous research on perceptions of school health care among school-aged children and adolescents with chronic diseases. Methods: This study was performed in accordance with Whittemore and Knafl's stages of an integrative review (problem identification, literature search, data evaluation, data analysis, and presentation of the results). Four databases (PubMed, CINAHL, Embase, and Web of Science) were used to retrieve relevant articles. Results: Eighteen articles were included in this review. We identified five thematic categories: peer-related issues, a safe school environment, self-perception of an existing disease, self-management, and a supportive school environment. Conclusion: It is necessary to establish a school health care system with a supportive environment for children and adolescents with chronic diseases.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.147-149
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• 2015

A nuclear power plant construction project normally involves a large construction work of which the total project cost is over 5 trillion Won, Such a large-scale construction project has the risks of schedule delay and quality degradation due to increase in project cost, because designs are changed due to design errors. The reasons for design changes during installation are 1) insufficient engineering capability of the owner, 2) information discontinuance due to the multiple package method, and 3) inefficient constructability review processes. Accordingly, this study proposes, through problem analysis, a method of developing a constructability review system that utilizes constructability review processes and a data-based technology (3D modeling) that are optimized for nuclear power plant construction projects. It also presents a method of establishing a system for reviewing constructability in which constructability review processes and a Database (3D model, Schedule, Design change Items) are linked each other.