• Journal of Sasang Constitutional Medicine
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• v.20 no.3
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• pp.82-93
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• 2008

1. Objective The purpose of this study was to develop and upgrade the On-line SSCQ (Sasang Constitution Questionnaire) by making an analysis of diagnostic accuracy rate of Sasang Constitution Questionnaire for doctors. 2. Methods We have collected SSCQ-D(Sasang Constitution Questionnaire for Doctors) from the dept. of Sasang constitutional medicine in the four other university. We classified data according to Sasang constitution, sex, age and BMI and made an analysis using the chiefly discriminant analysis model, additionally frequency analysis, and Cronbach's alpha coefficient. 3. Results and Conclusion 1) Diagnostic accuracy rate of the SSCQ-D was between 71.33 and 95.14%. (1) About the whole subject the accuracy rate was 71.33%. (2) About the whole female the accuracy rate was 73.26%. (3) About the whole male the accuracy rate was 81.41%. 2) The more classification variables we used in this analysis study, the higher the diagnostic accuracy rate increased.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.6.1-6.8
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• 2022

Background: With limited information available, the association among urinary tract infections, urease-producing bacteria and the presence of magnesium ammonium phosphate (MAP) urolithiasis in canines in Thailand requires more study. Objectives: This study aimed to investigate the association between demographic characteristics of canines and the presence of MAP urolithiasis in canines, and to evaluate antimicrobial susceptibility patterns of bacteria isolated from canine uroliths. Methods: A total of 56 canines admitted for treatment with surgical removal of uroliths were recruited. Demographic characteristics and clinical chemistry data were recorded. Bacteria isolated from the removed uroliths were identified. Chemical compositions of the uroliths were analyzed by Fourier transform infrared spectrometer. Potential risk factors were determined with univariable and multivariable logistic regression analyses. Results: Of 56 canine urolithiasis, bacteria were isolated from uroliths of 38 canines (27 MAP and 11 non-MAP) but not from uroliths of 18 canines (5 MAP and 13 non-MAP). The most common bacteria found in nidus of MAP uroliths was Staphylococcus pseudintermedius (approximately 51%). An antimicrobial resistance was frequently found in Staphylococci isolates (42.86%). Multivariate logistic regression analysis showed that the predictors of MAP urolith in canine urolithiasis were being female (p = 0.044; adjusted odds ratio [OR], 10.22; 95% confidence interval [CI], 1.06-98.24) and the positive urolith culture (p = 0.012; adjusted OR, 8.60; 95% CI, 1.60-46.30). Conclusions: Our results indicate that S. pseudintermedius (a urease-producing bacterium) is the major causative bacteria of MAP uroliths. A positive urolith culture and being female are risk factors of MAP urolithiasis in canines.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.149-157
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• 2014

Diabetes is a chronic disease that requires continuous medical care and patient-self management education to prevent acute complications and reduce the risk of long-term complications. The worldwide prevalence and incidence of diabetes mellitus are reached epidemic proportions in most populations. Early detection of diabetes could help to prevent its onset by taking appropriate preventive measures and managing lifestyle. The major objective of this research is to develop an automated decision support system for detection of diabetes using mixture of experts model. The performance of the classification algorithms was compared on the Pima Indians diabetes dataset. The result of this study demonstrated that the mixture of expert model achieved diagnostic accuracies were higher than the other automated diagnostic systems.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.121-128
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• 1994

In this paper, MIlS (Medical Image Information System) has been designed and implemented using INGRES RDBMS, which is based on a client/server architecture. The implemnted system allows users to register and retrieve patient information, medical images and diagnostic reports. It also provides the function to display these information on workstation windows simultaneously by using the designed menu-driven graphic user interface. The medical image compression! decompression techniques are implemented and integrated into the medical image database system for the efficient data storage and the fast access through the network.

• Journal of the korean academy of Pediatric Dentistry
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• v.46 no.1
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• pp.10-20
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• 2019

Individual dental age is used as an index of chronological age estimation and is an important indicator of the child's growth stage. Dental age does change greatly over time, but it changes constantly. And updating information about this change is important. The purpose of this study was to provide information about tooth eruption stage using diagnostic model analysis and to investigate tooth eruption sequence and estimate chronological age based on this information. Tooth eruption stages were measured on a diagnostic model from 488 patients in 5 - 13 year old children. Based on the information on eruption stage, eruption sequence in maxilla was first permanent molar, central incisor, lateral incisor, first premolar, canine, second premolar and second permanent molar. Eruption sequence in mandible was first permanent molar, central incisor, lateral incisor, canine, first premolar, second premolar and second permanent molar. There were significant differences between males and females in the eruption stage of canine, first and second premolar, and second molar at several ages. The chronological age of male and female was estimated by the coefficient of determination of 0.816, 0.826 respectively.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.237-250
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• 2002

Motivated by Cook's (1986) assessment of local influence by investigating the curvature of a surface associated with the overall discrepancy measure, this paper extends this idea to the linear regression model with AR(p) disturbances. Diagnostic for the linear regression models with AR(p) disturbances are discussed when simultaneous perturbations of the response vector are allowed. For the derived criterion, numerical studies demonstrate routine application of this work.

• The Journal of Korean Medicine
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• v.34 no.1
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• pp.69-79
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• 2013

Objectives: The aim of this survey was to investigate utilization, intended use, problems with and demand for medical devices by surveying members of the traditional Korean medical society. Methods: We distributed questionnaires to 13,957 traditional Korean medical doctors via e-mail, and received replies from 1,225. The questionnaire consisted of 4 multiple-choice questions for survey respondent information, 8 multiple-choice questions about the status of medical devices utilizing, and a short answer question about the demand for medical devices. Results: Use of medical devices in traditional Korean medical clinics is common. Diagnostic medical devices are mainly used to assess the patient's condition and to establish a close rapport with clients. In case of therapeutic medical devices, they are usually used for secondary treatment. Issues with traditional Korean medical devices currently in use were ineligibility for national health insurance, low reliability, uncertain validity, and high price. In development of traditional Korean medical equipment, the need for diagnostic medical devices was greater than for therapeutic, and the need for the recording and analysis of medical image data and visualization of medical information was great. Conclusions: There is growing demand for facilitating the development and commercialization of traditional Korean medical devices. To satisfy this demand, research on evaluation indicators that reflect functional and structural clinical information and how to clinically assess the indicators should proceed.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.157-163
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• 2017

This system is a diagnosis system that checks whether it causes a partial discharge of a power cable or not. PD(Partial Discharge) is detected by FBDS(Frequency Band Detection Sensor). That is, it means a acoustic sensor capable of detecting each frequency band. The wave shape of PD sound is similar to noise and is systematically generated by partial discharge. Therefore, in this paper, we could discriminate between normal and abnormal case using relative level crossing rate(RLCR) and spectrogram of frequency energy rate.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.6
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• pp.13-21
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• 2016

Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.