• Journal of Environmental Science International
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• v.25 no.1
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• pp.127-134
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• 2016

This study is conducted to get current state of data collection and utilization of the trees damaged by pine wilt disease, and to efficiently collect and utilize of the trees damaged by the pine wilt disease and abandoned fumigation-treated trees at forecasts. The method to control pine wilt disease damaged area is mostly fumigation treatment system, and there is no collection and utilization of damaged trees because of absence of efficient collection system, lack of collection cost, and absence of policy, etc. A survey is conducted that asked about the satisfaction degree for the trees damaged by pine wilt disease collection and use divided into 6 topics. It was positively recognized that the need and problem of the trees damaged by pine wilt disease, and results and satisfaction of forestry mechanization while it was mostly negatively recognized that the policy satisfaction for prevention and collection, satisfaction of collection and use, collection and forestry mechanization, and satisfaction of foundation equipment for forestry mechanization, etc. As a result of path analysis, it is necessary to promote a high-tech forestry mechanization to improve satisfaction level of results and satisfaction of forestry mechanization for the trees damaged by pine wilt disease collection and utilization, it is also need to make an effort in various ways for improve satisfaction level of satisfaction of collection and utilization.

• Journal of Preventive Medicine and Public Health
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• v.41 no.4
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• pp.219-224
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• 2008

Due to heightened concerns regarding possible bioterrorist attacks, the Korea Center for Disease Control and Prevention introduced syndromic surveillance systems, which have been run by emergency departments in hospitals throughout Korea since 2002. These systems are designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies, to mobilize a rapid response, and thereby to reduce morbidity and mortality. The Korea Center for Disease Control and Prevention performed drop-in syndromic surveillance successfully during the World Cup Football Games in 2002, the Universiad games in 2004, and the Asian Pacific Economic Cooperation meeting in 2005. In addition, sustainable syndromic surveillance system involving the collaborative efforts of 125 sentinel hospitals has been in operation nationwide since 2002. Because active data collection can bias decisions a physician makes, there is a need to generate an automatic and passive data collection system. Therefore, the Korea Center for Disease Control and Prevention plans to establish computerized automatic data collection systems in the near future. These systems will be used not only fur the early detection of bioterrorism but also for more effective public health responses to disease.

• International Journal of Oral Biology
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• v.46 no.1
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• pp.1-6
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• 2021

Since the outbreak of coronavirus disease 2019 (COVID-2019), the infection has spread worldwide due to the highly contagious nature of severe acute syndrome coronavirus (SARS-CoV-2). To manage SARS-CoV-2, the development of diagnostic assays that can quickly and accurately identify the disease in patients is necessary. Currently, nucleic acid-based testing and serology-based testing are two widely used approaches. Of these, nucleic acid-based testing with quantitative reverse transcription-PCR (RT-qPCR) using nasopharyngeal (NP) and/or oropharyngeal (OP) swabs is considered to be the gold standard. Recently, the use of saliva samples has been considered as an alternative method of sample collection. Compared to the NP and OP swab methods, saliva specimens have several advantages. Saliva specimens are easier to collect. Self-collection of saliva specimens can reduce the risk of infection to healthcare providers and reduce sample collection time and cost. Until recently, the sensitivity and accuracy of the data obtained using saliva specimens for SARS-CoV-2 detection was controversial. However, recent clinical research has found that sensitive and reliable data can be obtained from saliva specimens using RT-qPCR, with approximately 81% to 95% correspondence with the data obtained from NP and OP swabs. These data suggest that self-collected saliva is an alternative option for the diagnosis of COVID-19.

• Journal of Periodontal and Implant Science
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• v.53 no.5
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• pp.336-346
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• 2023

Purpose: The Korea Oral Biobank Network (KOBN) was established in 2021 as a branch of the Korea Biobank Network under the Korea Centers for Disease Control and Prevention to provide infrastructure for the collection, management, storage, and utilization of human bioresources from the oral cavity and associated clinical data for basic research and clinical studies. Methods: To address the need for the unification of the biobanking process, the KOBN organized the concept review for all the processes. Results: The KOBN established standard operating procedures for the collection, processing, and storage of oral samples. Conclusions: The importance of collecting high-quality bioresources to generate accurate and reproducible research results has always been emphasized. A standardized procedure is a basic prerequisite for implementing comprehensive quality management of biological resources and accurate data production.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1095-1105
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• 2013

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

• Korean Journal of Adult Nursing
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• v.23 no.4
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• pp.309-320
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• 2011

Purpose: The purpose of the study was to examine the related factors of quality of life (QOL) among patients with Chronic Obstructive Pulmonary Disease (COPD). Methods: Patients diagnosed with COPD (N=230) were recruited from four hospitals in Kyeong-Nam province, from March 2 to November 30, 2010. The data collection instruments were the Short Form 36, perceived dyspnea measure by Modified Medical Research Council, COPD and Asthma Sleep Impact Scale, COPD Self-efficacy Scale, and Center for Epidemiologic Studies Depression Scale were used. Following the completion of the data collection instruments Pulmonary function was tested. Data were analyzed with descriptive statistics, Pearson correlation and simultaneous multiple regression using SPSS/WIN. Results: The mean QOL of this study was 68.24. Using a multivariate approach, the significant correlates of QOL were depression (${\beta}$=-.37), dyspnea (${\beta}$=-.28), self-efficacy (${\beta}$=.20), and a sufficient degree of household income (${\beta}$=.16). These variables explained 49% of variance in QOL. Conclusion: The study suggests that psychological aspects are an important factor in explaining QOL of the patients. Screening and minimizing depression could be effective strategies in enhancing QOL of patients with COPD and further investigation to reduce depression could warrant the improvement of QOL in patients with COPD.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.93-100
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• 2024

With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.

• The Korean Journal of Nuclear Medicine
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• v.29 no.4
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• pp.478-483
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• 1995

We compared the influences of reconstruction methods using $180^{\circ}$ or $360^{\circ}$ data upon contrasts and discriminating capability and diagnostic accuracy in $^{99m}Tc-MIBI$ stress/rest myocardial SPECT. We reviewed SPECT images reconstructed only with $180^{\circ}$ projection data or with $360^{\circ}$ data in 18 patients and in 11 normal subjects. To compare counts of surface structures and deep structures, we measured ape# posterior wall ratios in 11 normal subjects. To compare the contrasts of images, we measured apex/ventricle ratios. To compare contrasts between normal and diseased myocardial segments, we measured count ratios of defect and normal segments in 4 patients who had single coronary artery diseases. To compare diagnostic accuracy, we scored SPECT images made with $180^{\circ}$ and $360^{\circ}$ data segmentally. Sensitivity and specificity for the diagnosis of coronary artery disease and for the revelation of diseased arteries with both $180^{\circ}$ and $360^{\circ}$ SPECT images. If involved coronary arteries had more narrowing than 50% In coronary angiogram, we considered them as diseased arteries Apex/posterior wall ratios were not different significantly in normal subjects. Apex/ ventricle ratios in normal subjects were different significantly between $180^{\circ}$ and $360^{\circ}$ SPECT images. Defect/normal ratios were different significantly between $180^{\circ}$ and $360^{\circ}$ SPECT images in single vessel disease patients. The overall diagnostic accurracy was the same between $180^{\circ}$ and $360^{\circ}$ data collection. Sensitivity was 94% and specificity was 91% for both types of data collection in this sample population. Sensitivity and specificity of each coronary artery territory were not significantly different between the images made with $180^{\circ}$ and $360^{\circ}$ data. The images made with $180^{\circ}$ data had better contrast between ventricle and myocardium and between hypoperfused and normal myocardium, though no difference was found between the ratios of the myocardial counts of surface and deep structures. However, diagnostic sensitivities of diseased artery territories were not different significantly and so were overall diagnostic accuracy between both methods of making images with $180^{\circ}$ and $360^{\circ}$ data.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Korean Journal of Adult Nursing
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• v.19 no.1
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• pp.12-23
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• 2007

Purpose: The purpose of the present study was to explore the meaning of quality of life in patients with chronic cardiovascular disease. Methods: A grounded theory method guided data collection and analysis. A total of 16 adult outpatients with chronic cardiovascular disease was participated. Data were collected through individual in-depth interviews. All interviews were audio taped and transcribed verbatim. Coding was used to establish different concepts and categories. A theoretical sampling technique was used to obtain diverse data from many relevant categories. Results: Seven categories were extracted, and they divided into constructing and intervening factors of quality of life. Constructing factors were uncertainty, recovery in the sense of control and maintaining social life. Intervening factors were symptom experiences, social support, taking care of themselves, and reflecting life. 'Keeping restrictive conditions under control' was emerged as a core category. The meaning of quality of life in patients with cardiovascular was explained according to the levels of keeping restrictive conditions under control. Conclusion: The result of this study may contribute for health professionals to understand the quality of life in patients with cardiovascular disease.