• Journal of Microbiology
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• v.33 no.2
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• pp.95-102
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• 1995

A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the chuster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. Theresults show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Journal of Digestive Cancer Research
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• v.10 no.2
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• pp.56-64
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• 2022

Narrow-band imaging (NBI) is the most widely used image-enhanced endoscopic technique. The superficial microanatomy of gastric mucosa can be visualized when used with a magnifying endoscopy with narrow-band imaging (ME-NBI). The diagnostic criteria for early gastric cancer (EGC), using the classification system for microvascular and microsurface pattern of ME-NBI, have been developed, and their usefulness has been proven in the differential diagnosis of small depressed cancer from focal gastritis and in lateral extent delineation of EGC. Some studies reported on the prediction of histologic differentiation and invasion depth of gastric cancer using ME-NBI; however, its application is limited in clinical practice, and further well-designed studies are necessary. Clinicians should understand the ME-NBI classification system and acquire appropriate diagnostic skills through various experiences and training to improve the quality of endoscopy for EGC diagnosis.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• 대한상한금궤의학회지
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• v.5 no.1
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• pp.19-29
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• 2013

Objective : The purpose of this study is to analyze the naming of 'A diagnostic system based on Shanghanlun six meridian patterns and provisions' and to suggest an alternative naming. Methods : 1. The meaning of 'Six meridian(六經)' was reviewed on existing theories and Shanghanlun provisions. 2. Comparing the name of diangostic system with the term in 'Korean Standard Classification of Diseases-6(KCD-6)' and term in 'WHO international standard terminologies on traditional medicine in the western pacific region' was done. Results : 'Six meridian' is customary used in the Shagnhanlun study but its meaning is not match with original Shanghanlun system and could possibly make misunderstanding. So 'Disease pattern identification' is suitable than 'Six meridian' for this diagnostic system. Conclusions : This study suggests that using 'A disease pattern identification diagnostic system based on Shanghanlun provisions.'is more appropriate instead of using the name of the six meridian diagnostic system.

• Childhood Kidney Diseases
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• v.27 no.2
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• pp.64-69
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• 2023

Membranoproliferative glomerulonephritis (MPGN) is a complex group of renal diseases characterized by a specific pattern of glomerular injury that includes thickening of the capillary wall and mesangial expansion, leading to a heterogeneous group of conditions. This review article offers a comprehensive overview of MPGN, its new classification, pathophysiology, diagnostic evaluation, and management options.

• Korean Journal of Transplantation
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• v.31 no.4
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• pp.170-176
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• 2017

We need to establish an informative guideline to increase inter-institutional and inter-observer reproducibility of renal transplant diagnosis, and to improve the diagnostic ability of pathologists in Korea. A first nation-wide survey for renal transplant pathology was conducted by Renal Pathology Study Group of the Korean Society of Pathologists in 2016, to provide the continued excellence in the transplantation pathology laboratory, and to improve the diagnostic ability for the best treatment of transplant patients. This survey revealed the significant variations in scale, work load and biopsy indications for the renal transplant pathology in various institutions in Korea. The Banff classification were used by all institutions for the diagnosis of renal transplant pathology, but different formats were used: most institutions (70%) used the "2013 Banff classification" while the others were using "2007 Banff classification" (20%) or even older formats. In daily diagnostic practice of the renal allografts, difficulties that pathologists encounter were quite diverse due to different environments they work in. Most respondents agreed that standardized diagnostic practice guidelines, regular education on renal transplant pathology and convenient ways of consultation are further needed. We are currently working toward the enhancement of the expertise of renal pathologists and to increase inter-institutional and inter-observer reproducibility by 1) development of a set of virtual slides of renal allograft biopsies for the training, 2) validation and gathering expert's consensus on the core variables of rejection diagnosis by using virtual slides, and 3) continued education by the developed virtual slide atlas.

• The Journal of Korean Medicine
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• v.35 no.3
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• pp.1-14
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• 2014

Objectives: The purpose of this study was to evaluate the achievements of Ryodoraku and to suggest new diagnostic ideas. Methods: A search of six Korean databases and one Japanese was performed using the terms such as Ryodoraku, diagnosis, foreigner, etc. and the search results classified and summarized. Results: From the initial search results, 21 Korean papers and 26 Japanese papers were selected and classified into 4 categories, that is, classification by pattern, classification by physiological limit, classification by setting various sections using the average Ryodoraku score, and classification by formula. Conclusions: Each of the 4 methods has its own benefits; however, it is hard to find disease-specific common characteristics from the Ryodoraku diagnostic values with the methods. Further studies which use the number of Pyesaek and Gyeokcha are necessary.