• Korean Medical Education Review
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• v.25 no.2
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• pp.114-118
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• 2023

Multiple cohorts (e.g., current students and graduates) were formed to collect information on the entire educational process from admission to graduation regarding students' educational performances at Kosin University College of Medicine. Data that had already been collected and analyzed by different committees for different purposes were grouped into a more systematic and comprehensive system called the cohort system, enabling the necessary data to be collected promptly and analyzed in accordance with the purpose of providing meaningful information in each area of the educational process. Therefore, comprehensive cohort data that can be used for mission statement revision, curriculum development and improvement, student counseling, and student selection were established and utilized. The cohort data were collected from performance evaluation indicators including self-evaluation surveys, evaluation tools for learning outcomes, academic achievement, results of the Korean Medical Licensing Examination, and career placement. Based on the results obtained by analyzing cohort data, a comprehensive cohort report has been published. The data analyzed through the cohort were reported to each committee and used in various ways. Currently, however, only some data have been analyzed and used. In the future, after complete data collection, the cohort data can be used as meaningful basic data for achieving the institution's mission and educational goals, developing and improving the curriculum, counseling students, and selecting students through the analysis of learning performance data from student admission to graduation and after graduation.

• Journal of the Korea Society for Simulation
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• v.25 no.1
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• pp.29-34
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• 2016

Many researchers have used cohort DB (database) to predict the occurrence of disease or to keep track of disease spread. Cohort DB is Big Data which has simply stored disease and health information as separated DB table sets. To measure the relations between health information, It is necessary to reconstruct cohort DB which follows research purpose. In this paper, XML descriptor, editor has been used to construct ontology-based Big Data cohort DB. Also, we have developed ontology based cohort DB search system to check results of relations between health information. XML editor has used 7 layered Ontology development 101 and OWL API to change cohort DB into ontology-based. Ontology-based cohort DB system can measure the relation of disease and health information and can be used effectively when semantic relations are found. We have developed ontology-based cohort DB search system which can measure the relations between disease and health information. And it is very effective when searched results are semantic relations.

• Korean Medical Education Review
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• v.25 no.2
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• pp.119-125
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• 2023

Pusan National University School of Medicine (PNUSOM) began analyzing the cohort of pre-medical students admitted in 2015 and has been conducting purposeful analyses for the past 3 years. The aim of this paper is to introduce the process of cohort establishment, cohort composition, and the utilization of cohort analysis results. PNUSOM did not initially form a cohort with a purpose or through a systematic process, but was able to collect longitudinal data on students through the establishment of a Medical Education Information System and an organization that supports medical education. Cohort construction at our university is different in terms of a clear orientation toward research questions, flexibility in cohort composition, and subsequent guideline supplementation. We investigated the relevance of admission factors, performance improvements, satisfaction with the educational environment, and promotion and failure rate in undergraduate students, as well as performance levels and career paths in graduates. The results were presented to the Admissions Committee, Curriculum Committee, Learning Outcomes Committee, and Student Guidance Committee to be used as a basis for innovations and improvements in education. Since cohort studies require long-term efforts, it is necessary to ensure the efficiency of data collection for graduate cohorts, as well as the validity and ethics of the study.

• Speech Sciences
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• v.9 no.2
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• pp.135-146
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• 2002

Generally a speaker verification system improves its system recognition ratio by regularizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. The speaker-based cohort method is one of the methods that are widely used for selecting background speaker model. Recently, Gaussian-based cohort model has been suggested as a virtually synthesized cohort model, and unlike a speaker-based model, this is the method that chooses only the probability distributions close to basic speaker's probability distribution among the several neighboring speakers' probability distributions and thereby synthesizes a new virtual speaker model. It shows more excellent results than the existing speaker-based method. This study compared the existing speaker-based background speaker models and virtual speaker models and then constructed new virtual background speaker model groups which combined them in a certain ratio. For this, this study constructed a speaker verification system that uses GMM (Gaussin Mixture Model), and found that the suggested method of selecting virtual background speaker model shows more improved performance.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.274-283
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• 2023

Objective: To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts. Materials and Methods: A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared. Results: A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001). Conclusion: DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.464-474
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• 2022

Objectives: We introduced the cohort studies included in the Korean Cohort Consortium (KCC), focusing on large-scale cohort studies established in Korea with a prolonged follow-up period. Moreover, we also provided projections of the follow-up and estimates of the sample size that would be necessary for big-data analyses based on pooling established cohort studies, including population-based genomic studies. Methods: We mainly focused on the characteristics of individual cohort studies from the KCC. We developed "PROFAN", a Shiny application for projecting the follow-up period to achieve a certain number of cases when pooling established cohort studies. As examples, we projected the follow-up periods for 5000 cases of gastric cancer, 2500 cases of prostate and breast cancer, and 500 cases of non-Hodgkin lymphoma. The sample sizes for sequencing-based analyses based on a 1:1 case-control study were also calculated. Results: The KCC consisted of 8 individual cohort studies, of which 3 were community-based and 5 were health screening-based cohorts. The population-based cohort studies were mainly organized by Korean government agencies and research institutes. The projected follow-up period was at least 10 years to achieve 5000 cases based on a cohort of 0.5 million participants. The mean of the minimum to maximum sample sizes for performing sequencing analyses was 5917-72 102. Conclusions: We propose an approach to establish a large-scale consortium based on the standardization and harmonization of existing cohort studies to obtain adequate statistical power with a sufficient sample size to analyze high-risk groups or rare cancer subtypes.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.13-24
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• 2017

The Sample Cohort DB supplied by the National Health Insurance Service is a valuable resource for statistical studies as well as for health and medical studies. It takes significant time and effort to extract data from this Cohort DB having a large size. As such, we introduce a database system, conveniently called the National Health Insurance Service Cohort DB Extract Tool (NICE Tool), which supports several useful operations for effectively and efficiently managing the Cohort DB. For example, researchers can extract variables and cases related with study by simply clicking a computer mouse without any prior knowledge regarding SAS DATA step or SQL. We expect that NICE Tool will facilitate the faster extraction of data and eventually lead to the active use of the Cohort DB for research purposes.

• Journal of Preventive Medicine and Public Health
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• v.55 no.3
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• pp.253-262
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• 2022

We aimed to review the current data composition of the Korean Tuberculosis and Post-Tuberculosis Cohort, which was constructed by linking the Korean Tuberculosis Surveillance System (KNTSS; established and operated by the Korean Disease Control and Prevention Agency since 2000) and the National Health Information Database (NHID; established by the National Health Insurance Service in 2012). The following data were linked: KNTSS data pertaining to patients diagnosed with tuberculosis between 2011 and 2018, NHID data of patients with a history of tuberculosis and related diseases between 2006 and 2018, and data (obtained from the Statistics Korea database) on causes of death. Data from 300 117 tuberculosis patients (177 206 men and 122 911 women) were linked. The rate of treatment success for new cases was highest in 2015 (86.7%), with a gradual decrease thereafter. The treatment success rate for previously treated cases showed an increasing trend until 2014 (79.0%) and decreased thereafter. In total, 53 906 deaths were confirmed among tuberculosis patients included in the cohort. The Korean Tuberculosis and Post-Tuberculosis Cohort can be used to analyze different measurement variables in an integrated manner depending on the data source. Therefore, these cohort data can be used in future epidemiological studies and research on policy-effect analysis, treatment outcome analysis, and health-related behaviors such as treatment discontinuation.

• Korean Medical Education Review
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• v.25 no.2
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• pp.132-138
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• 2023

Cohorts are established and operated at medical schools as part of efforts to improve the quality of education. Chosun University College of Medicine clarified the purpose of establishing three cohorts in light of its core values and developed criteria and indicators for each purpose. An organization focusing on cohort construction and operation was established as the Cohort Committee under the Quality Improvement Committee, and guidelines were proposed. In addition, a database and system were developed to handle primary data efficiently, and tools for measuring psychological variables were created. The data collected by establishing a cohort, regions, and admission types of graduates were first analyzed for the following projects: (1) an analysis of the educational process and quality improvement to educate medical professionals who contribute to the community after graduation, and (2) an analysis of the educational process and quality improvement to secure excellence in the medical field (e.g., knowledge and clinical reasoning), using information on the academic achievements of students and graduates as primary data. Chosun University College of Medicine is conducting cohorts and longitudinal studies gradually, starting with a simple, practically feasible system to solve the difficulties faced in cohort establishment and operation. Medical educators hope that future data collection and analysis will improve the quality of medical school education and have practical implications.

• Journal of Environmental Science International
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• v.17 no.5
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• pp.517-524
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• 2008

The purpose of this study was to investigate the characteristics of air quality using data from which obtain local air quality monitoring system for cohort study in Chungju, Korea. We analyzed the concentration data of $NO_2,\;SO_2$, and $PM_{10}$ in Chungju and industrial cities in 2006. We compared a industrial area with a cohort study area using by bicluster algorithm. In the case of $SO_2$, the rate of the cluster time was $10{\sim}60%$ and the cluster time number of two areas was similar. In the case of $NO_2$ and $PM_{10}$, the number of cluster time between a industrial area and cohort study area was clearly different.