• Journal of Chest Surgery
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• v.49 no.sup1
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• pp.44-52
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• 2016

Background: This study investigates the perception of the general public regarding the concentration to metropolitan, hospitals of cardiac and cerebrovascular surgeries, and the perceived public need for government policies to resolve this issue. Methods: A total of 800 participants were recruited for our telephone interview survey. Quota sampling was performed, adjusting for age and sex, to select by various geographic regions. Sampling with random digit dialing was performed; we called the randomly generated telephone numbers and made three attempts for non-responders before moving on to a different telephone number. Results: Our sample population was 818 participants, 401 men (49.0%) and 417 women (51.0%). Our data showed that 85.5% of participants thought that cardiac surgery and neurosurgery patients are concentrated in large hospitals in Seoul. The principle reason for regional patients to want to receive surgery at major hospitals in Seoul was because of poor medical standards associated with regional hospitals (87.7%). We found that a vast majority of participants (97.5%) felt that government policies are needed to even out the clustering of cardiac surgery and neurosurgery patients, and that this clustering may be alleviated if policies that can specifically enhance the quality and the capacity of regional hospitals to carry out surgeries are adopted (98.3%). Conclusion: Government policy making must reflect public desiderata, and we suggest that these public health needs may be partially resolved through government-designated cardiac and neurosurgery specialist hospitals in regional areas.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.381-393
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• 2024

Purpose Metabolic abnormalities in hepatic encephalopathy (HE) cause brain edema or demyelinating disease, resulting in symmetric regional cerebral edema (SRCE) on MRI. This study aimed to investigate the usefulness of the clustering analysis of SRCE in predicting the development of brain failure. Materials and Methods MR findings and clinical data of 98 consecutive patients with HE were retrospectively analyzed. The correlation between the 12 regions of SRCE was calculated using the phi (φ) coefficient, and the pattern was classified using hierarchical clustering using the φ² distance measure and Ward's method. The classified patterns of SRCE were correlated with clinical parameters such as the model for end-stage liver disease (MELD) score and HE grade. Results Significant associations were found between 22 pairs of regions of interest, including the red nucleus and corpus callosum (φ = 0.81, p < 0.001), crus cerebri and red nucleus (φ = 0.72, p < 0.001), and red nucleus and dentate nucleus (φ = 0.66, p < 0.001). After hierarchical clustering, 24 cases were classified into Group I, 35 into Group II, and 39 into Group III. Group III had a higher MELD score (p = 0.04) and HE grade (p = 0.002) than Group I. Conclusion Our study demonstrates that the SRCE patterns can be useful in predicting hepatic preservation and the occurrence of cerebral failure in HE.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.539-550
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• 2018

Purpose: The purpose of this study was to find meaningful patient groups of disease using foreign patients data and analyze implemented test of the patient groups. Methods: The data was collected by foreign patients' EMR data of K university hospital. The author proposed tree-form patients' characteristic diagram through statistical methods that association rule, proportion test, clustering using prescription information and questionnaire information. Results: This study's analysis process was applied high blood data and diabetes data. Analysis showed other characteristic of meaningful patient groups in high blood and diabetes. In high blood, test implementation rate of patient group showed the differences. And in diabetes, test implementation rate of patient group and implemented test list showed differences. Conclusion: The result of this study can play a role as basic data that can be clinical testing standard in preventive aspect. Eventually, 5 dimensions of SERVQUAL will be improved by this study's process.

• Tuberculosis and Respiratory Diseases
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• v.87 no.1
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• pp.1-11
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• 2024

Acute respiratory distress syndrome (ARDS) is a common cause of severe hypoxemia defined by the acute onset of bilateral non-cardiogenic pulmonary edema. The diagnosis is made by defined consensus criteria. Supportive care, including prevention of further injury to the lungs, is the only treatment that conclusively improves outcomes. The inability to find more advanced therapies is due, in part, to the highly sensitive but relatively non-specific current syndromic consensus criteria, combining a heterogenous population of patients under the umbrella of ARDS. With few effective therapies, the morality rate remains 30% to 40%. Many subphenotypes of ARDS have been proposed to cluster patients with shared combinations of observable or measurable traits. Subphenotyping patients is a strategy to overcome heterogeneity to advance clinical research and eventually identify treatable traits. Subphenotypes of ARDS have been proposed based on radiographic patterns, protein biomarkers, transcriptomics, and/or machine-based clustering of clinical and biological variables. Some of these strategies have been reproducible across patient cohorts, but at present all have practical limitations to their implementation. Furthermore, there is no agreement on which strategy is the most appropriate. This review will discuss the current strategies for subphenotyping patients with ARDS, including the strengths and limitations, and the future directions of ARDS subphenotyping.

• Molecules and Cells
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• v.44 no.7
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• pp.433-443
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• 2021

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.295-301
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• 2017

In this study, We subtracted the calcification blooming artifact from MDCT images of coronary atherosclerosis patients and verified their accuracy and usefulness. We performed coronary artery calcification stenosis phantom and a program to subtract calcification blooming artifact by applying 8 different image segmentation method (Otsu, Sobel, Prewitt, Canny, DoG, Region Growing, Gaussian+K-mean clustering, Otsu+DoG). As a result, In the coronary artery calcification stenosis phantom with the lumen region 5 mm the calcification blooming artifact was subtracted in the application of the mixture of Gaussian filtering and K- Clustering algorithm, and the value was close to the actual calcification region. These results may help to accurately diagnose coronary artery calcification stenosis.

• Journal of Korean Neurosurgical Society
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• v.54 no.5
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• pp.390-398
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• 2013

Objective : We determined whether the relationship between the neuropsychological performance of patients with mild traumatic brain injury (TBI) and their psychopathological characteristics measured by disability evaluation are interrelated. In addition, we assessed which psychopathological variable was most influential on neuropsychological performance via statistical clustering of the same characteristics of mild TBI. Methods : A total of 219 disability evaluation participants with mild brain injury were selected. All participants were classified into three groups, based on their psychopathological characteristics, via a two-step cluster analysis using validity and clinical scales from the Minnesota Multiphasic Personality Inventory (MMPI) and Symptom Checklist-90-revised (SCL-90-R). The Korean Wechsler Adult Intelligence Scale (K-WAIS), Korean Memory Assessment Scale (K-MAS) and the Korean Boston Naming Test (K-BNT) were used to evaluate the neurocognitive functions of mild TBI patients. Results : Over a quarter (26.9%) experienced severe psychopathological symptoms and 43.4% experienced mild or moderate psychopathological symptoms, and all of the mild TBI patients showed a significant relationship between neurocognitive functions and subjective and/or objective psychopathic symptoms, but the degree of this relationship was moderate. Variances of neurocognitive function were explained by neurotic and psychotic symptoms, but the role of these factors were different to each other and participants did not show intelligence and other cognitive domain decrement except for global memory abilities compared to the non-psychopathology group. Conclusion : Certain patients with mild TBI showed psychopathological symptoms, but these were not directly related to cognitive decrement. Psychopathology and cognitive decrement are discrete aspects in patients with mild TBI. Furthermore, the neurotic symptoms of mild TBI patients made positive complements to decrements or impairments of neurocognitive functions, but the psychotic symptoms had a negative effect on neurocognitive functions.

• Genomics & Informatics
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• v.14 no.2
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• pp.46-52
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• 2016

Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.