• Journal of Microbiology and Biotechnology
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• v.20 no.10
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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.

• The Journal of Internal Korean Medicine
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• v.36 no.4
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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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• v.13 no.3
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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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• v.14 no.4
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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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• v.6 no.1
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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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• v.56 no.3
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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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• v.64 no.2
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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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• v.12 no.3
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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.

• Health Policy and Management
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• v.2 no.1
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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.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.