• 생물정신의학
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• 제23권3호
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• pp.80-88
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• 2016

Vascular dementia is a very frequent form of dementia. Debates over classification and diagnostic criteria, and controversy over identifiable treatment targets will continue until distinct pathophysiological mechanism of vascular dementia is found. Clinical diagnostic criteria are sufficiently strong to be useful for clinical trials, but need further refinement. Cognitive changes in vascular dementia are more variable than other disorders, and are dependent on the vascular pathology. Accurate diagnosis of vascular dementia is known to need the presence of reliable cerebrovascular disease on brain imaging. Although it seems obvious that cerebrovascular disease causes pathological damage and impaired cognition, it is very difficult to find the accurate contribution of cerebrovascular pathology to cognitive decline. Most studies have shown a small but significant benefit of cholinesterase inhibitors on cognition, the significance of this effect has been slight and benefits on global functioning, activities of daily living, and behaviour have not been consistently reported. Management of vascular dementia should focus on identifying and managing vascular risk factors.

• 한국임상수의학회:학술대회논문집
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• 한국임상수의학회 2009년도 춘계학술대회
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• pp.127-133
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• 2009

Cardiomyopathies were previously defined as "an idiopathic myocardial disease that is not secondary to any other type of congenital/acquired heart disease or systemic diseases." With increasing understanding of etiology and pathogenesis in human medicine, the difference between cardiomyopathy and specific heart muscle disease has become indistinct. Cardiomyopathies are now classified by the dominant pathophysiology or, if possible, by etiological/pathogenetic factors. The American Heart Association recently advocated the following new definition of cardiomyopathy: Cardiomyopathies are a heterogeneous group of diseases of the myocardium associated with mechanical and/or electrical dysfunction that usually (but not invariably) exhibit inappropriate ventricular hypertrophy or dilatation and are due to a variety of causes that frequently are genetic. Cardiomyopathies either are confined to the heart or are part of generalized systemic disorders, often leading to cardiovascular death or progressive heart failure-related disability. Because the understanding of etiology or pathogenesis of cardiomyopathy has been limited in veterinary medicine, the previous classification is generally used. It is considered a dilated, hypertrophic and restrictive group on the basis of the predominant morphological and functional abnormalities. In addition, arrhythmogenic right ventricular cardiomyopathy and unclassified cardiomyopathy were also recognized in dogs and/or cats.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권4호
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• pp.220-224
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• 2000

A new auscultation system for the detection of breath sound form trachea was developed in house. Small size microphone(panasonic pin microphone) was encapsuled in a housing for resonant effect, and hardware for the sound detection was fabricated. Pulmonary function test results were compared with the parameters extracted from frequency spectrum of breath sound obtained from the developed system. Results showed that the peak frequency and relative ratio of integral values between low(80∼400Hz) and high(400∼800Hz) frequency ranges revealed the significant differences. Developed system could be used for distinguishing normal subject and the patients who have pulmonary disease.

• 사상체질의학회지
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• 제26권3호
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• pp.224-240
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• 2014

Objectives This research was proposed to present Clinical Practice Guideline(CPG) for Soyangin Disease of Sasang Constitutional Medicine(SCM): Diagnosis and Algorithm. This CPG was developed by the national-wide experts committee consisting of SCM professors. Methods We searched the literature and articles related to Soyangin Symptomatology diagnosis and algorithm. For developing diagnosis and algorithm, we searched the classification, ordinary symptom, present symptom of the Soyangin Symptomatology. Results & Conclusions We classify the Soyangin Symptomatology by 4 steps: Exterior-Interior disease, favorable-unfavorable pattern, mild-moderate-severe-critical pattern (initial-advanced pattern). And at the unfavorable pattern, ordinary symptom is very important. So doctors need to focus on the symptom of unfavorable's ordinary symptom such as temperament inclined symptom, diarrhea, and diurnal body fever.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 추계학술대회
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• pp.405-407
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• 2017

The environmental change is affecting the farm products like tomato, and pepper, etc. This affects to lead smart farming yield. What is more, this inconstant conditions cause the farms to be infected by variety diseases. Therefore ICT technology is needed to detect and prevent the crops from being effected by diseases. This article suggests the procedure to help producer for identifying farms disease based on the detected image. This detects the kind of diseases with comparing the trained image data before and after disease emergence. First step monitors an image of farms and resize it. Its features are extracted on parameters such as color, and morphology, etc. The next steps are used for classification to classify the image as infected or non-infected. on the bassis of detection algorithm.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.519-527
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• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• Journal of Genetic Medicine
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• 제18권2호
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• pp.83-93
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• 2021

Charcot-Marie-Tooth disease (CMT) is the most common hereditary motor and sensory peripheral neuropathy. CMT is usually classified into two categories based on pathology: demyelinating CMT type 1 (CMT1) and axonal CMT type 2 (CMT2) neuropathy. CMT1 can be distinguished by assessing the median motor nerve conduction velocity as greater than 38 m/s. The main clinical features of axonal CMT2 neuropathy are distal muscle weakness and loss of sensory and areflexia. In addition, they showed unusual clinical features, including delayed development, hearing loss, pyramidal signs, vocal cord paralysis, optic atrophy, and abnormal pupillary reactions. Recently, customized treatments for genetic diseases have been developed, and pregnancy diagnosis can enable the birth of a normal child when the causative gene mutation is found in CMT2. Therefore, accurate diagnosis based on genotype/phenotypic correlations is becoming more important. In this review, we describe the latest findings on the phenotypic characteristics of axonal CMT2 neuropathy. We hope that this review will be useful for clinicians in regard to the diagnosis and treatment of CMT.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.

• 센서학회지
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• 제32권6호
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.