• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.4
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• pp.718-724
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• 2008

In order to classify diseases of oriental medicine in liaison with International Classification of Diseases, there should be intermediation and sharing concepts between the two in addition to proper classification. Classification units were settled for differentiation of diseases or syndromes first. And second, the standard forms of disease classification system were proposed. Third, this classification system was made of serial groupings of syndrome under the traditional disease name. Fourth, the location of disease and the interrelation between different syndromes were depicted with diagram in order to define more clearly. As the results and conclusion, The classification units were composed of 2 categories; topology, organ, meridian, somatic structure, body fluid units for description and various regulatory unit terms of western and traditional medicine for explanation. The mixed classification model of western diseases and traditional syndromes(證) was adopted as a fundamental classification system containing disease by exterior pathogen, systemic internal diseases, psychoneuronal diseases, metabolic diseases, diseases of sense organs, supportive structure diseases, obstetric-gynecology diseases, child diseases, 4-type constitutional diseases. And those were differentiated with generalized, localized, functional, oncogenic, environmental features in detail. The cause, site, condition, dispositions must be expressed in each disease name too. The types of diagnosis using classification system are principal and final diagnosis, principal procedure, main conditions, and these are applied to this Korean classification system equally. For more clarification of differentiation, a plane topological map and three dimensional coordinates were proposed to manifest the location, features and relation of disease itself or each other.

• The Journal of the Korean life insurance medical association
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• v.27 no.1
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• pp.21-23
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• 2008

The 5th revision of Korean Classification of Diseases(KCD) became effective on January 1, 2008. It has reflected the changes made to the tenth revision of International Classification of Diseases (ICD-10) between 1998 and 2005 and the suggestions of academic and related societies in Korea. Two important alterations seem to have a major implication in the insurance industry. One would be the official introduction of a Korean version of International Classification of Diseases for Oncology, third edition(ICD-O-3). The borderline ovarian tumor is classified as a borderline neoplasm, which was classified as a malignant neoplasm in the previous edition of International Classification of Diseases for Oncology. The other would be the appearance of non-C-code malignant neoplasm for the diseases, such as polycythemia vera, newly classified as a malignant neoplasm by the current edition of International Classification of Diseases for Oncology. The National Office of Statistics(NSO) adopted the way of implementation used in the Australian Modification of International Classification of Diseases(ICD-10-AM), instead of assigning them into corresponding C code. Overall, the changes made in this revision doesn't seem to have a serious impact on the insurance industry since it has only reflected updates made to ICD-10.

• The Journal of Korean Medicine
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• v.21 no.3
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• pp.9-19
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• 2000

Objective : We proposed fundamental rules of prospective Korean Standard Classification of Diseases(Oriental Medicine). Methods : We analysed Korean Standard Classification of Diseases(Oriental Medicine)(established in 1994) in comparison with ICD-10 and Chinese Standard Classification of Disease(Traditional Chinese Medicine). Secondly, we analysed the diagnostic structure of Modem oriental medicine. Results : Korean Standard Classification of Diseases has an inappropriate writing structure, logical errors of classification, confusion of symptoms, 'bing', and 'zheng', inappropriate comparison of disease designations in oriental medicine and western medicine, and the ommission of important items. Secondly, we demonstrate the relations of 'bing' and 'zheng' in modem oriental medicine and disease designations in oriental medicine and western medicine. Conclusions : We propose the separate classification of 'bing' and 'zheng', the qualification of designated names, the structure of 'bing' and 'zheng' system, and a different writing method.

• Journal of Haehwa Medicine
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• v.8 no.1
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• pp.97-114
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• 1999

By studying disease classifications of oriental medicine from Nei-Ching, Chao's-Bing-Yuan, Dong-Yi-Bao-Jian and Korea-standard classification of causes of disease & death. The results were obtained as follows : 1. In Nei-Ching 181 kinds, Chao's-Bing-Yuan 1729 kinds, Dong-Yi-Bao-Jian 966 kinds, and Korea-standard classification of causes of disease & death 2519 kinds of diseases, which suggested more diseases as time flew. 2. In classical books such as Nei-Ching, Chao's-Bing-Yuan, and Dong-Yi-Bao-Jian most of diseases and their names were originated from six kinds of pathogenic factors, Zang-Fu, Jung-Qi-Blood-Fluid, soul, and outer-body-signs, while Korea-standard classification of causes of disease & death classified diseases according to oriental medical departments. 3. Symptoms of Cold-Heat-Excess-Deficiency and pathogenic factors, body parts, Zang-Fu were applied to names of diseases in oriental medicine. 4. In oriental medicine, some symtoms, many intermal diseases were used as disease name, but it is necessary for us to select exact name of diseases in modem clinical treatment. 5. We should consider disease names in Korea-standard classification of causes of disease & death in relations with western medical terms of diseases.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.17 no.3
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• pp.1-7
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• 2004

Objectives: In order to establish a base for proper treatment and management of patients with dermal problems through correct diagnosis, I considered the naming rule for dermatology in Oriental Medicine, referring to the dermatology literature compared to western medicine. In addition, this paper examines the characteristic classification of dermatology. Methods: I examined the naming rule of dermatology in Oriental Medicine and then compared the disease names in Oriental and Western medicine and the characteristic classification of dermatology referred to the records. Results: The dermal diseases have been named according to their colors and morphologies, causes, progress of symptoms, recurrent sites, the character of distribution, recurrent seasons, ages, the character of patients' jobs and locations. Sometimes some have been named by referring to their main morphologies, sites, causes, colors and seasons synthetically. However it was found some names for dermal diseases, even though the same diseases, had been named differently according to for example: historical times, condition of locations and the quality of doctors whose process of naming developed and changed over time. The relationship between Oriental and Western medicine of each name for dermal diseases is basically divided into 5 types: same names - same diseases; same names but different diseases; same diseases but different names; one disease with multiple names; and one name with multiple diseases. Considering the methods of classification, these were generally achieved according to their places of origin. It is a method unique to Oriental medicine that we classified some dermal diseases into 疥, 癬, 瘡, 風, 丹, 疱, 疹, 癰, 痘, 疽 and so on and it is very easy to diagnose which part they belong to. This was classified by putting first the causes of diseases; for instance: viruses, bacteria, fungi. Sometimes, however there was a problem, connected to the classification of morphology. Conclusions: I suggest that we need to unify and refine dermatological terms in Oriental Medicine in order to establish a base for proper treatment and management of patients with dermal problems through correct diagnoses.

• Journal of Society of Preventive Korean Medicine
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• v.19 no.2
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• pp.113-121
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• 2015

Objective : This study was aimed to classify diseases that Traditional Chinese Medicine(TCM) is better at treating. Method : Literature was searched on China National Knowledge Infrastructure(CNKI) and categorized according to literature type, published date and research method. Studied six types of research papers and four types of published books. Results : Experts were surveyed and interviewed, medical records were studied retrospectively, and doubleblind method was used in selecting diseases that TCM was better at treating. There were a total of 372 diseases that TCM was better at treating. By the KCD classification, 45 were in gastrointestinal (12.1%), 39 in urogenital (10.5%), 36 in circulatory (9.7%), 35 in musculoskeletal or connective tissues (9.4%). Conclusion : Total of 372 diseases were classified as diseases that TCM was better at treating, and if the results are used adequately, the values of western and TCM can be maximized and benefit the government, patients and the medical practitioners.

• Journal of Society of Preventive Korean Medicine
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• v.4 no.2
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• pp.93-107
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• 2000

Korea follows the Korea standard classification of disease and causes of death according to the ICD(international classification of disease) Oriental medicine began to of officially follow the classification of disease for using the Korean classification of diseases in 1972. The classification of OM(oriental medicine) has changed in shape experiencing two amendments. The largest difficulty was to overcome the different names of diseases between OM and ICD. A one-to-one correspondence of the name of a disease between OM and ICD is impossible So in the primary stage one-to-one and one-to-many correspondence was made. During the first amendment the international disease names were re-classified on the oriental medicine disease name's basis and at the same time the classification of OM was corresponded on a one-to-one basis to the ICD . During the second amendment this changed to many-to-many correspondence . Analyzing the history of classification of OM during the first and second amendments, it was discovered that establishment of the standards of classification, the unification of oriental medical terms, and overcoming the difference of disease names between the OM and ICD is necessary Also th classification and standardazation of OM must not stop as a single round. It must go on for a long time. The hosts of this project Korean oriental medical society and AKOM(association of korean oriental medicine) need to build a independant department which will supervise the classification project and monitor any problems to come up. Also a route through which suggestions can be taken in and new solutions can be brought up needs to be secured and an atmosphere in which studies can take place about the basis of classifications needs to be developed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• 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.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.