• Journal of Microbiology and Biotechnology
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• 제20권10호
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• pp.1457-1462
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• 2010

Three types of serotypically atypical Shigella flexneri isolates were collected between 2007 and 2008 from Korean patients at the Korea National Institute of Health (NIH). These atypical isolates were characterized and compared with serologically typical S. flexneri. The first grouping of 11 atypical isolates displayed agglutination only with polyB antiserum and exhibited no reaction with any typing or grouping sera (PolyB:un). The second group of 3 isolates displayed reactions with typing sera IV, but also did not bind with any grouping sera (IV:un). The third group of 14 isolates exhibited a plural agglutination pattern, reacting with typing sera II, and two grouping sera (II:(3)4,7(8)). Amongst these atypical isolates, isolates belonging to IV:un and II:(3)4,7(8) exhibited greater antibiotic resistance, in particular to ampicillin, streptomycin, and trimethoprim-sulfamethoxazole, than typical S. flexneri strains. Furthermore, all II:(3)4,7(8) strains harbored integrons. This study suggests that these multiple antibiotic-resistant atypical S. flexneri are new subserotypes of S. flexneri that await further serological classification.

• 대한한방내과학회지
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• 제36권4호
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• pp.561-569
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• 2015

Objectives The purpose of this study was to improve the knowledge of the low-level laser therapy (LLLT) field and to review research reports on LLLT to understand the current situation with respect to the clinical indication and current research trends.Methods A survey was carried out on the subject of low-level laser therapy to September 2012, using the PubMed search engine. Selected literature was checked by two reviewers and was classified according to the International Classification of Diseases 10th (ICD-10) over 10 years.Results We selected 469 studies in total, of which 142 were case reports, 118 were case-controlled trials, and 209 were randomized controlled trials of LLLT. According to the ICD-10 classification of diseases, the K code and M code being the most common, 399 studies have been published in the last 10 years. This shows that the study and clinical indications of low-level laser therapy have rapidly increased over the past 10 years.Conclusions Low-level laser therapy has been used most frequently with respect to dentistry and pain and musculoskeletal disorders. Recently, interest in and research into LLLT has increased for various diseases. With the establishment of standard conditions for low-level laser therapy, supported by aggressive clinical utilization and systematic clinical research, LLLT will be a very useful treatment and a useful alternative method in many medical fields.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.92-103
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• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• International Journal of Contents
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• 제6권1호
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• pp.59-64
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• 2010

Recently, biological researches are required to deal with a large scale of data. While scientists used classical experimental approaches for researches in the past, it is possible to get more sophisticated observations easily with the convergence of information technologies and biology. The study on diseases is one of the most important issues of the life science. Conventional services and databases provide users with information such as classification of diseases, symptoms, and medical treatments through the Web. However, it is hard to connect or develop them for other new services because they have independent and different criteria. It may be a factor that interferes the development of biology. In this paper, we propose integrated data structures for the disease databases. We also design and implement a novel directory system for diseases as an infrastructure for developing the new diseases services.

• Clinical Endoscopy
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• 제56권3호
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• pp.325-332
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• 2023

Background/Aims: Administrative databases provide valuable information for large-cohort studies. This study aimed to evaluate the diagnostic accuracy of an administrative database for resected gastric adenomas. Methods: Data of patients who underwent endoscopic resection for benign gastric lesions were collected from three hospitals. Gastric adenoma cases were identified in the hospital database using International Classification of Diseases (ICD) 10-codes. The non-adenoma group included patients without gastric adenoma codes. The diagnostic accuracy for gastric adenoma was analyzed based on the pathological reports of the resected specimen. Results: Among 5,095 endoscopic resections with codes for benign gastric lesions, 3,909 patients were included in the analysis. Among them, 2,831 and 1,078 patients were allocated to the adenoma and non-adenoma groups, respectively. Regarding the overall diagnosis of gastric adenoma with ICD-10 codes, the sensitivity, specificity, positive predictive value, and negative predictive value were 98.7%, 88.5%, 95.2%, and 96.8%, respectively. There were no significant differences in these parameters between the tertiary and secondary centers. Conclusions: Administrative codes of gastric adenoma, according to ICD-10 codes, showed good accuracy and can serve as a useful tool to study prognosis of these patients in real-world data studies in the future.

• Clinical and Experimental Pediatrics
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• 제64권2호
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• pp.80-85
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• 2021

Background: Although identifying cases in large administrative databases may aid future research studies, previous reports demonstrated that the use of the International Classification of Diseases, Tenth Revision (ICD-10) code alone for diagnosis leads to disease misclassification. Purpose: We aimed to assess the value of the ICD-10 diagnostic code for identifying potential children with biliary atresia. Methods: Patients aged <18 years assigned the ICD-10 code of biliary atresia (Q44.2) between January 1996 and December 2016 at a quaternary care teaching hospital were identified. We also reviewed patients with other diagnoses of code-defined cirrhosis to identify more potential cases of biliary atresia. A proposed diagnostic algorithm was used to define ICD-10 code accuracy, sensitivity, and specificity. Results: We reviewed the medical records of 155 patients with ICD-10 code Q44.2 and 69 patients with other codes for biliary cirrhosis (K74.4, K74.5, K74.6). The accuracy for identifying definite/probable/possible biliary atresia cases was 80%, while the sensitivity was 88% (95% confidence interval [CI], 82%-93%). Three independent predictors were associated with algorithm-defined definite/probable/possible cases of biliary atresia: ICD-10 code Q44.2 (odds ratio [OR], 2.90; 95% CI, 1.09-7.71), history of pale stool (OR, 2.78; 95% CI, 1.18-6.60), and a presumed diagnosis of biliary atresia prior to referral to our hospital (OR, 17.49; 95% CI, 7.01-43.64). A significant interaction was noted between ICD-10 code Q44.2 and a history of pale stool (P<0.05). The area under the curve was 0.87 (95% CI, 0.84-0.89). Conclusion: ICD-10 code Q44.2 has an acceptable value for diagnosing biliary atresia. Incorporating clinical data improves the case identification. The use of this proposed diagnostic algorithm to examine data from administrative databases may facilitate appropriate health care allocation and aid future research investigations.

• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.102-107
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• 2020

Recently, the number of people visiting the hospital is increasing due to diabetes. According to the Korean Diabetes Association, statistically, 1 in 7 adults over the age of 30 are suffering from diabetes. As such, diabetes is one of the most common diseases among modern people. In this paper, in addition to blood sugar, which is widely used for diabetes awareness, BMI, which is known to be related to diabetes, triglycerides and cholesterol that cause various complications in diabetics it was studied using random forest techniques and decision trees known to be effective for classification. The importance of each element was confirmed using the results and characteristic importance derived using two techniques. Through this, we studied the diabetes-related relationship between BMI, triglyceride, and cholesterol as well as blood sugar, a factor that diabetic patients should pay much attention to.

• 보건행정학회지
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• 제2권1호
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• pp.66-79
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• 1992

The code of the International Classification of Disease(ICD) is seriously questioned on its effectiveness in identifing an independent disease entity from similar conditions at general practitioner's offices. This study has attempted to show individual coding variations in ICD for similar ambulatory care conditions. It has been assumed that a following outpatient visit is regarded as the sane kind of visit owing to the same disease if a visit to the different source of care would be mad within an interval of less than two days. The 'D' health insurance association was selected for this analysis. The 'D' association had 153,298 members and made claims of 642,605 outpatient care in 1990. Out of the total outpatient claims, 8.6%(55,102 claims) were counted as the same disease which could meet the above assumption. Percent of conditions classified as the 10 leading causes of frequent visits which were matched accurately to the subsequent ICD diagnostic code found to be 15.8% on the average. The URI was noted for the highest concurrence rate of 20.4%. This proportion was even decreased to 11.6% on the case of chronic disease. Despite the fact that the assumption underlying the definition of the above same disease is rather rough and inappropriate, this study reveals that the code of ICD currently in use has weaknesses in seperating a certain independent disease from similar conditions at the outpatient setting. Thus, efforts need to be elaborated to meet the need of a new system of classification for conditions and diseases encountering at ambulatory care.

• 지능정보연구
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• 제26권3호
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• pp.127-147
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• 2020

세계는 신형 코로나바이러스 감염증(COVID-19)으로 수 많은 인명 피해와 경제적 손실을 기록하고 있는 상황이다. 우리나라 정부는 연구개발(Research & Development)을 통해 국가 감염병 위기를 극복하려는 전략을 수립하고 실행하기 위한 투자방향을 수립하였다. 기존 기술분류나 과학기술 표준분류에 따른 통계를 활용하면 특정 R&D 분야의 특이점 및 변화를 발견하기 어렵다는 한계가 존재해왔다. 최근 우리나라 감염병 연구개발 과제를 대상으로 수요자의 목적에 맞게 분류체계를 수립하고 연구비 비교 분석을 통해 투자가 요구되는 연구 분야를 제시하는 연구들이 진행되었다. 하지만 현재 국가 보건 안보와 신성장 산업육성이라는 목표를 달성하기 위한 실행방안으로 요구되고 있는 전염병 연구분야의 국가간 협력전략 수립에 필요한 정보를 체계적으로 제공하고 있지 못한 상황이다. 따라서 국가 공동 연구개발 전략 수립을 위한 분류체계와 분류모델기반의 정보서비스에 대한 연구가 요구되고 있다. 우선 감염병관련 NTIS 과제데이터를 기반으로 정성분석을 통해 7개의 분류체계를 도출하였다. 스코퍼스(Scopus) 데이터와 양방향 RNN모델을 사용하여, 분류체계 모델을 학습시켰다. 최종적인 모델의 분류 성능은 90%이상의 높은 정확도와 강건성을 확보하였다. 실증연구를 위해 주요 국가의 코로나바이러스 연구개발 과제를 대상으로 전염병 분류체계를 적용하였다. 주요 국가의 감염병(코로나바이러스) 연구개발 과제를 분류체계별로 분석한 결과, 세계적으로 유행하는 바이러스의 예상치 못한 창궐이 확산되는 속도에 비해 백신과 치료제 개발이 제대로 이뤄지지 않는 원인의 배경을 간접적으로 확인할 수 있었다. 국가별 비교분석을 통해 미국과 일본은 상대적으로 모든 영역에 골고루 연구개발 투자를 하고 있는 것으로 나타난 반면, 유럽은 상대적으로 특정 연구분야에 많은 투자를 하는 집중화 전략을 취하는 것으로 나타났다. 동시에 주요 국가의 코로나 바이러스 주요 연구조직에 대한 정보를 분류체계별로 제공하여 국제 공동R&D 전략의 기초정보를 제공하였다. 본 연구 결과를 통해 세 가지 정책적 의미를 도출할 수 있다. 첫째, 데이터기반 과학기술정책 관점에서 수요자 관심분야에 대한 국가 R&D사업의 정보를 글로벌 기준으로 문서를 분류하는 방안을 제시하였다. 둘째, 감염병관련 국가 R&D사업 영역에 대한 정보분석 서비스 기획의 기반을 마련하였다. 마지막으로 국가 감염병 R&D 분류체계 수립을 통해 분류 체계의 궁극적 목표인 산업, 기업, 정책 정보를 제공할 수 있는 기반을 마련한 것이다.