• 사상체질의학회지
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• 제23권4호
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• pp.429-444
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• 2011

1. Objective This study is aimed to present the effective classification of Soeumin symptomatology and the standardized signs for classification which can be applied for KCD, ICD and the insurance codification system. 2. Methods 1) Differentiate Soeumin symptomatology based on exterior-interior patterns, favorable-unfavorable patterns, and mild-severe-dangerous-urgent patterns. 2) Investigate the standard signs and symptoms to claasify Soeumin symptomatology based on exterior-interior patterns, favorable-unfavorable patterns, and mild-severe-dangerous-urgent patterns. 3. Results and Conclusions 1) The diagnosis criteria for Soeumin exterior-interior disease is based upon signs & symptoms of cold/heat, condition of stool, state of digestive system(such as digestion and appetite)among others. 2) The diagnosis criteria for Soeumin favorable-unfavorable disease is generally based upon whether the vital force of the spleen is damaged or not. More specifically, for the exterior disease, whether or not sweating is present. For the interior disease, whether or not dry mouth, body ache(a main symptom of the exterior state), and anxiousness are present. 3) For the Soeumin Wool-gwang disease, the diagnosis criteria of mild-severe disease is whether or not chills is present and the degree of body fever. 4) For Soeumin Mang-yang disease, the diagnosis criteria of dangerous-urgent disease is whether or not chills is, the degree of sweating and urine condition. 5) For the Soeumin Greater-Yin disease, Abdominal-pain bowel irritability pattern and Epigastric discomfort pattern are early state signs, Jaundice pattern is mild-state sign, edema & Greater-Yang disease Yin-toxin pattern are terminal state signs. 6) For the Soeumin interior disease, Abdominal-pain bowel irritability pattern and Epigastric discomfort pattern are of the dangerous state pattern, Jang-gual and Exuberant-Yin-repelling-Yang pattern are of the urgent state patterns.

• 동의생리병리학회지
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• 제16권2호
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• pp.209-214
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• 2002

At this paper, I classified ‘tetanus’ in 『DongEuiBoGam』 and studied the concept, causes, symptoms, pathological mechanisms of that disease and then I had a new understanding that concept of tetanus in 『DongEuiBoGam』 is different with concept of tetanus in Western Medicine. In the mean time, I investigated the classification in 「Classification of Korean Standard Cause of Death(Oriental Medicine)」 (1995, The Korean Economic Planning Board), and concluded the concept of tetanus in "DongEuiBoGam".

• 사상체질의학회지
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• 제13권2호
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• pp.49-61
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• 2001

The purpose of this article was to compare 'The Discourse on the Constitutional Symptoms and Disease' of ${\ulcorner}$Dongyi Soose Bowon${\lrcorner}$ written in 1894(Old Edition(舊本)) with that of ${\ulcorner}$Dongyi Soose Bowon${\lrcorner}$ published in 1901(In Edition(印本)), and to find the idea of pathologic mechanism and classification of 'the Exterior and Interior disease'. the conclusions were as follows. 1. The classification of constitutional symptoms and disease of Soeumin and Soyangin in 'Old Edition(舊本)' was almost equal to that in 'In Edition(印本)' 2. In pathological mechanism of constitutional symptoms and disease of Soeumin and Soyangin, 'The Exterior Disease' could be explained as the disease resulted from fight between 'Yang-chi(陽氣)(Hot-chi(熱氣))'of 'Thoracic vertebrae' and 'Yin-chi(陰氣)(Cold-chi(寒氣))' of 'Bladder' and 'The Interior Disease' between 'Hot-chi(熱氣)(Stomach-chi(胄氣))' of 'Stomach' and 'Cold-chi(寒氣)' of 'Large intestine'. 3. 'The Exterior Symptoms and Disease of the Exterior and the Interior Disease(表裏之表病)' could be explained as the disease occurring at the Branch portion(large portion)(標) by overcoming of Pathogenic factors but Vital energy still sufficient, and 'The Interior Symptoms and Disease of the Exterior and the Interior Disease(表裏之裏病)' occurring at Root portion(small portion)(裏) by invasion of Pathogenic factors and Vital energy almost exhausted. 4. In the classification of constitutional symptoms and disease of Taeumin, 'The Exterior Symptoms and Disease of the Exterior and the Interior Disease(表裏之表病)' in 'Old Edition(舊本)' were rearranged to 'The Exterior Disease' in 'In Edition(印本)', 'The Interior Symptoms and Disease of the Exterior and the Interior Disease(表裏之裏病)' to 'The Interior Disease'. 5. It was assumed that 'The Exterior and the Interior Disease' of Taeumin could be explained in relation between the exterior and e interior, based on the Healthy energy(保命之主) and e concept of the Branch and the Root portion

• ETRI Journal
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• 제37권2호
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• 전자공학회논문지CI
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• 제48권4호
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• pp.21-26
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• 2011

특징 순위 방법은 데이터에 대한 정보와 관련된 특징을 구별하는데 유용하게 사용된다. 본 논문에서는 혈소판으로부터 측정된 라만 스펙트럼에서 퇴행성 뇌신경질환과 혈관성 인지증의 분류에 특징 순위를 이용하는 방법을 제안하였다. 퇴행성 뇌신경 질환인 알츠하이머병(Alzheimer's disease)과 파킨슨병(Parkinson's disease) 그리고 혈관성 인지증(vascular dementia)을 유도한 실험용 쥐의 혈소판에서 측정한 스펙트럼은 가우시안 모델을 이용한 커브 피팅으로 노이즈를 제거하고 로컬 최저점에 선형 보간법(linear interpolation)으로 배경 잡음을 제거한다. 전처리 과정을 수행한 스펙트럼에서 분류정확도와 계산복잡도를 개선하기 위해 특징 순위 방법을 이용하여 주요 특징을 선택하였다. 선택된 특징들은 PCA(principal component analysis) 방법으로 변환하여 주성분의 수를 변화시키며 MAP(maximum a posteriori)으로 분류하고 전체 특징을 사용한 경우의 분류 결과와 비교하였다. 실험 결과에서 제안한 방법을 적용한 모든 실험에서 분류 시스템의 계산복잡도를 현저하게 감소시키고 분류정확도는 부분적으로 증가하였다. 특히 파킨슨병과 정상을 분류하는 실험에서 제안한 방법이 전체 특징을 사용한 경우보다 모든 주성분의 수에서 분류정확도가 높았으며 평균 1.7 %의 성능이 향상되었다. 이 결과에서 분류정확도와 계산복잡도의 개선을 고려하면 제안한 방법이 혈소판 라만 스펙트럼에서 퇴행성 뇌신경질환과 혈관성 인지증의 분류 시스템에 효율적으로 사용될 수 있음을 확인하였다.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• 한국직업건강간호학회지
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• 제28권2호
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• pp.75-82
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• 2019

Purpose: This study aimed to determine the influence of injury and/or injured area classification on depression in patients with industrial injuries. Methods: The participants comprised438 patients who consented to participate and completed self-reported questionnaires. Data were analyzed using SPSS/WIN version 22.0 for descriptive statistics, $x^2$ test, fisher's exact test, ANOVA, and post-hoc $Scheff{\acute{e}}$ test. A stepwise multiple regression analysis was used to identify factors influencing depression. Results: The results indicated that the effect of disease classification and injured areas on depression were significantly different in patients with industrial injuries. The results further showed that severe depression was significantly higher in cardiovascular patients and patients with an injured area of the head and waist. The most powerful predictor was age (50~59 years), return to work (reemployment), disease classification (cardiovascular), and injured area (head, including vascular disease). Conclusion: This study showed that the most influential variable of depression in patients with industrial injuries were cardiovascular issues, injury areas of the head and waist, being aged 50~59 years, and reemployment. To reduce depression in these patients, it is important to develop and implement a psychiatric rehabilitation program that helps patients to formulate a concrete plan and goal for recovery, enabling patients to actively engage in their rehabilitation.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• 대한예방한의학회지
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• 제17권2호
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• pp.47-68
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• 2013

Objectives : This research aimed to develop a new etiologic classification for traditional Korean Medicine in order to respond to the social and environmental change. Methods : We reviewed the existing theories on etiological classification for East Asian Medicine thoroughly and discussed the problems and limitations. Based on the experts' consensus, we abstracted disease factors and etiologic items. Results : The disease factors are classified into three parts: the human body, the environment, and the interaction between the human body and the environment. We defined them as the inner factor, the external factor, and the interaction between the inner and the external factors. The inner factor is free from the influence of the environment, and it causes diseases solely from the components of the human body. It is divided into genetic factors. The external factor is defined as a case when a disease occurs due to a factor outside the human body and includes external injuries, environmental pollution, and natural disasters. The interaction between the inner and the external factors is a disease factor that causes diseases by the interaction of the human body and the environment and includes emotions, habits, and social environment. As a result of the analysis, it was possible to see the meanings at a single glance as the scattered and fractional meanings were integrated with focus on medicinal herbs, but the increasing number of analyzed medicinal herbs tended to more and more complicate their relationships, thus, requiring additional work like filtering. Conclusions : The new etiologic classification of Korean Medicine fully reflects the perspectives on life in Korean Medicine while embracing the changes in modem society. Also, by avoiding the usage of ambivalent terms and wrong classification methods, the new classification system constructs intuitive and concise etiology and improves usability in clinical medicine.