• Journal of Korean Neurosurgical Society
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• 제67권3호
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• pp.308-314
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• 2024

Vein of Galen aneurysmal malformation is one of important pediatric arteriovenous shunt diseases, especially among neonates and infants. Here, early history of the disease identification, basic pathoanatomy with a focus on the embryonic median prosencephalic vein, classification and differential diagnoses, and recent genetic studies are reviewed.

• 한국콘텐츠학회논문지
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• 제9권7호
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• pp.1-8
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• 2009

본 논문에서는 질병에 대한 전문적인 지식이 부족한 일반인들을 대상으로 자신의 건강 상태를 쉽게 파악할 수 있는 퍼지 신경망 기법을 이용한 한방 자가 진단 질병 분류 시스템과, 자택에서 간편하게 전문의의 진료상담을 받을 수 있는 원격 진료 시스템을 통합한 홈메디컬 시스템을 제안한다. 제안한 한방 자가 진단 시스템은 72가지 한방 질병과 각 질병에 대한 증상을 분석하여 데이터베이스로 구축하고 구축된 데이터베이스 정보를 기반으로 퍼지 신경망 기법을 적용하여 사용자의 질병을 도출한다. 본 논문의 자가 진단 방법은 사용자가 자신의 대표 증상을 제시하면 해당 증상을 포함하는 질병들을 도출하고, 도출된 질병들의 세부 증상들을 사용자가 입력 벡터로 제시하면 퍼지 신경망 기법을 적용하여 세부 증상에 대한 질병들을 클러스터링한 후, 세부 증상에 대한 질병의 소속 정도를 제공한다. 제안한 원격 진료 시스템은 사용자와 전문의가 모두 로그인을 통하여 접속하게 되면 서버에 클라이언트의 정보가 송신되고, 사용자는 서버에서 전문의의 접속 현황을 전달받아 원하는 전문의와 동화상으로 원격 연결되어 전문의의 진료 소견을 받는다. 본 논문에서 제안한 시스템을 한의학 전문의가 분석한 결과, 본 논문에서 제안한 시스템이 한방 질병의 보조 진단으로서의 가능성을 확인하였다.

• Childhood Kidney Diseases
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• 제16권1호
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• pp.21-31
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• 2012

초점성 분절성 사구체 경화증(focal segmental glomerulosclerosis; FSGS)은 경화증을 주 병변으로 하는 질환으로서 일차성 사구체 질환의 하나이면서 진행된 사구체 질환의 형태적 변화를 기술하는 단어로도 사용되고 있다. 사구체에는 경화증, 유리질 형성, 거품세포의 출현, 발세포의 공포화, 광륜형성 등이 보이고, 간질의 섬유화와 염증세포의 침윤, 세뇨관의 위축, 혈관의 비후 및 내막 섬유화 등을 특징으로 한다. 면역형광검사에서 부분적으로 IgM과 C3 등의 침착을 보이지만 면역관련 질환은 아니다. 전자현미경 검사에서는 발세포의 손상 현상으로 세포질 내의 공포화와 족돌기가 상실되는 것이 중요 소견이다. 2004년 표준화 된 FSGS의 분류는 과거의 형태학적 변형들을 모아서 임상과의 상관관계를 지웠다. 그 결과 tip형이 가장 예후가 좋으며, collapsing형이 가장 나쁜 것으로 알려졌다. 그러나 이 분류가 증례에 따라서는 적용하기가 애매한 경우가 많고, collapsing형을 FSGS에 분류하는 것에 대한 반론 등이 제기되고 있다. 한편, 임상적으로는 FSGS를 원인에 따라 분류하여 거꾸로 형태학적 공통점을 찾으려는 노력을 하고 있다. 사구체의 수가 적어서 일어나는 과여과로 인한 FSGS는 perihilar형이 많고 유전적 질환에 의한 것은 diffuse mesangial sclerosis가 특징인 것으로 주장되고 있다. FSGS는 이와 같이 아직도 밝혀져야 할 것이 많은 질환이며, 계속적인 연구가 이루어져야 할 필요가 있다.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제17권4호
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• pp.223-231
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• 2014

Purpose: With previous methods based on only age and location, there are many difficulties in identifying the etiology of acute abdominal pain in children. We sought to develop a new systematic classification of acute abdominal pain and to give some helps to physicians encountering difficulties in diagnoses. Methods: From March 2005 to May 2010, clinical data were collected retrospectively from 442 children hospitalized due to acute abdominal pain with no apparent underlying disease. According to the final diagnoses, diseases that caused acute abdominal pain were classified into nine groups. Results: The nine groups were group I "catastrophic surgical abdomen" (7 patients, 1.6%), group II "acute appendicitis and mesenteric lymphadenitis" (56 patients, 12.7%), group III "intestinal obstruction" (57 patients, 12.9%), group IV "viral and bacterial acute gastroenteritis" (90 patients, 20.4%), group V "peptic ulcer and gastroduodenitis" (66 patients, 14.9%), group VI "hepatobiliary and pancreatic disease" (14 patients, 3.2%), group VII "febrile viral illness and extraintestinal infection" (69 patients, 15.6%), group VIII "functional gastrointestinal disorder (acute manifestation)" (20 patients, 4.5%), and group IX "unclassified acute abdominal pain" (63 patients, 14.3%). Four patients were enrolled in two disease groups each. Conclusion: Patients were distributed unevenly across the nine groups of acute abdominal pain. In particular, the "unclassified abdominal pain" only group was not uncommon. Considering a systemic classification for acute abdominal pain may be helpful in the diagnostic approach in children.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.993-1002
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• 2016

Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of Acupuncture Research
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• 제25권5호
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• pp.43-57
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• 2008

Objectives : The purpose of this study is to find the principal of the assignment of Sidong disease and Sosaeng disease(是動病 所生病) into 12 meridians and suggest the author's opinion. Methods : 1. The authors investigated the conception of Sidong disease and Sosaeng disease through several literatures. 2. The authors investigated the line course of 12 meridians(經脈流注) and their Sidong disease and Sosaeng disease. 3. The authors classified Sidong disease and Sosaeng disease following the study by Kim et al. 4. The authors suggested the opinions about the diseases that are difficult to be understood direct relation with the course of meridian. Results : 1. The result of classification of Sidong disease and Sosaeng disease into 5 shows that the percentages were 32.96% for meridian's own disease(本經病), 13.97% for organic own disease(本臟腑病), 12.85% for other organic own disease(他臟腑病), 20.67% for related organic disease(有關器官病), 19.55% for etc.(其他病). 2. Therefore, 19.55% of the whole Sidong disease and Sosaeng disease is that which occurred on the site that is not related directly with the meridian. Conclusions : 1. The exterior and interior relation(表裏關係) and mutual communication between organ and bowel(臟腑相通) are associated with the basic principal of the assignment of Sidong disease and Sosaeng disease that is not related with the course of meridian. 2. The cause of assignment of Sidong disease and Sosaeng disease can be explained according to the profound medical theories.

• 동의생리병리학회지
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• 제25권6호
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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.

• 대한두경부종양학회지
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• 제18권2호
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• pp.179-183
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• 2002

Objective: A comparison of American Joint Committee on Cancer (AJCC) 1988 and 1997 nasopharyngeal carcinoma (NPC) classifications was made in terms of patient distribution and efficacy in predicting prognosis. Materials and Methods: Between Jan. 1981 and Dec. 1998, 60 cases of node negative nasopharyngeal carcinoma were retrospectively reviewed. The extent of disease each patients restaged according to the 4th and 5th AJCC system and Ho system, respectively. Results: The overall and disease free 5-year survival rates were 61.1% and 62.6%, respectively. Among T classifications of 4th AJCC, 5th AJCC and Ho staging system were not observed significantly different in disease-free survival rates, respectively. Conclusion: We observed a better patient distribution with AJCC 1997 comparing to AJCC 1988. The new classification also attained better statistical significances among stages in the overall survival and disease free survival rates was needed.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.