• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.809-816
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• 2010

This paper proposed about a load-balanced multi-agent model in mobile distribution environment to monitor moving patients and to deal with a situation of emergency. This model was designed to have a structure based on distribution framework by expanding a mobile system, and provides healthcare services based on real time situational information on moving patients. In order to overcome the limitation of middleware when we design system, we provided an abstract layer between applications and their base network infrastructure so that balance between QoS requests and network life can be maintained. In addition, clustering was used in cells for the efficient load distribution among multi-agents. By using Clustering FCM, we got optimal resources and had solve about transmission delay.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.195-197
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• 2011

This research deals with an issue of preventive medicine in bioinformatics. We can diagnose liver conditions reasonably well to prevent Liver Cirrhosis by classifying liver disorder patients into fatty liver and high risk groups. The classification proceeds in two steps. Classification rules are first built by clustering five attributes (MCV, ALP, ALT, ASP, and GGT) of blood test dataset provided by the UCI Repository. The clusters can be formed by the K-mean method that analyzes multi dimensional attributes. We analyze the properties of each cluster divided into fatty liver, high risk and normal classes. The classification rules are generated by the analysis. In this paper, we suggest a method to diagnosis and predict liver condition to alcoholic patient according to risk levels using the classification rule from the new results of blood test. The K-mean classifier has been found to be more accurate for the result of blood test and provides the risk of fatty liver to normal liver conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.830-836
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• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.941-948
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• 2016

In this paper, we efficiently simulated for collecting the data from the fixed sensor and mobile sensor of patients using the LEACH-Mobile method. The LEACH-Mobile method is the protocol to increase the mobility by adding the mobile node to the existed LEACH(:Low Energy Adaptive Clustering Hierarchy) protocol. It improves the mobility of The LEACH-Mobile in the LEACH, however it consumes more energy than the existed LEACH. There is reason why we use the LEACH-Mobile that the monitoring system is generally done by the CCTV and an periodic checkup by nurses. However the number of nurse is a few in the most of hospital. It can happen the accidents of the patients in out of the CCTV when the nurse can not see the monitoring system in the hospital. Therefore it is simulated to continuously gather the data of the position and sensors in the five situation of moving the patients in the hospital, it gets the result that the management of the mobile patients is more efficient.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.255-262
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• 2006

$\underline{Purpose}$: To evaluate the pathological prognostic factors related to local recurrence after radical surgery and adjuvant radiation therapy in advanced rectal cancer. $\underline{Materials\;and\;Methods}$: Fifty-four patients with advanced rectal cancer who were treated with radical surgery followed by adjuvant radiotherapy and chemotherapy between February 1993 and December 2001 were enrolled in this study. Among these patients, 14 patients experienced local recurrence. Tissue specimens of the patients were obtained to determine pathologic parameters such as histological grade, depth of invasion, venous invasion, lymphatic invasion, neural invasion and immunohistopathological analysis for expression of p53, Ki-67, c-erb, ezrin, c-met, phosphorylated S6 kinase, S100A4, and HIF-1 alpha. The correlation of these parameters with the tumor response to radiotherapy was statistically analyzed using the chi-square test, multivariate analysis, and the hierarchical clustering method. $\underline{Results}$: In univariate analysis, the histological tumor grade, venous invasion, invasion depth of the tumor and the over expression of c-met and HIF-1 alpha were accompanied with radioresistance that was found to be statistically significant. In multivariate analysis, venous invasion, invasion depth of tumor and over expression of c-met were also accompanied with radioresistance that was found to be statistically significant. By analysis with hierarchical clustering, the invasion depth of the tumor, and the over expression of c-met and HIF-1 alpha were factors found to be related to local recurrence. Whereas 71.4% of patients with local recurrence had 2 or more these factors, only 27.5% of patients without local recurrence had 2 or more of these factors. $\underline{Conclusion}$: In advanced rectal cancer patients treated by radical surgery and adjuvant chemo-radiation therapy, the poor prognostic factors found to be related to local recurrence were HIF-1 alpha positive, c-met positive, and an invasion depth more than 5.5 mm. A prospective study is necessary to confirm whether these factors would be useful clinical parameters to measure and predict a radio-resistance group of patients.

• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.5
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• pp.788-803
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• 2021

This study aims to obtain basic data to develop clothes for dementia patients by classifying types of dementia patients. Data was collected from those dementia patients who entered a nursing home. This study analyzed a total of 221 sheets. Furthermore, descriptive statistics, cross-tabulation, and K-means clustering were performed for statistical processing using Minitab 14. As a result, dementia patients were classified into four types: inactive-dependent, active-problematic behavior, activity-autonomy, and inactive-offensive. Inactive-dependent type was a group with the most severe disability in cognitive and daily activity functions; however, they lacked behavioral and psychological symptoms and problematic behavior with clothes. Active-problematic behavior type showed the most behavioral and psychological problems and problematic behavior with clothes. Activity-autonomy type was a group without any problematic behaviors. Moreover, the inactive-offensive type had very good cognitive function toward humans. The study imply that it is necessary to provide clothes with proper functions based on the types of patients rather than providing them uniform clothes because clinical and clothes behaviors differ significantly depending on the types of dementia patients.

• Journal of Korean Neurosurgical Society
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• v.67 no.3
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• pp.299-307
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• 2024

Cerebral cavernous malformation (CCM) is a vascular anomaly commonly found in children and young adults. Common clinical presentations of pediatric patients with CCMs include headache, focal neurological deficits, and seizures. Approximately 40% of pediatric patients are asymptomatic. Understanding the natural history of CCM is crucial and hemorrhagic rates are higher in patients with an initial hemorrhagic presentation, whereas it is low in asymptomatic patients. There is a phenomenon known as temporal clustering in which a higher frequency of symptomatic hemorrhages occurs within a few years following the initial hemorrhagic event. Surgical resection remains the mainstay of treatment for pediatric CCMs. Excision of a hemosiderin-laden rim is controversial regarding its impact on epilepsy outcomes. Stereotactic radiosurgery is an alternative treatment, especially for deep-seated CCMs, but its true efficacy needs to be verified in a clinical trial.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.33-38
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• 2016

Disease of patient who visited the hospital can cause different symptoms of the disease, depending on the environment and lifestyle. Recent medical services offered in patients has changed in the environment that can be selected for treatment by analyzing the patient according to the disease symptoms. In this paper, we propose an efficient method to manage disease control because the treatment method may change at any patients suffering from the disease according to the patient conditions by grouping the different treatments to patients for disease information. The proposed scheme has a feature that can be ingested by the patient big disease information, as well as to improve the treatment efficiency of the medical treatment the increase patient satisfaction. The proposed sheme can handle big data by clustering of disease information for patients suffering from diseases such as patient consent small groups. In addition, the proposed scheme has the advantage that can be conveniently accessed via a particular keyword, the treatment method according to patient disease information. The experimental results, the proposed method has been improved by 23% in terms of efficiency compared to conventional techniques, disease management time is gained 11.3% improved results. Medical service user satisfaction seen from the survey is to obtain a high 31.5% results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.180-185
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• 2015

Digital infrared thermal imaging is a non-invasive adjunctive diagnostic technique that allows an examiner to visualize and quantify changes in skin surface temperature. The asymmetry of temperature differences between the diseased and the contralateral healthy body parts can be automatically analyzed and has been studied in many areas of medical science. In this paper, we propose a method for intelligent automatic asymmetry detection based on a K-means analysis and a YCbCr color model. The implemented software successfully visualizes an asymmetric distribution of colors with respect to the patients’ health status.