• Journal of Veterinary Clinics
• /
• v.31 no.4
• /
• pp.288-292
• /
• 2014

A critical assumption of the standard sample size calculation is that the response (outcome) for an individual patient is completely independent to that for any other patient. However, this assumption no longer holds when there is a lack of statistical independence across subjects seen in cluster randomized designs. In this setting, patients within a cluster are more likely to respond in a similar manner; patient outcomes may correlate strongly within clusters. Thus, direct use of standard sample size formulae for cluster design, ignoring the clustering effect, may result in sample size that are too small, resulting in a study that is under-powered for detecting the desired level of difference between groups. This paper revisit worked examples for sample size calculation provided in a previous paper using nomogram to easy to access. Then we present the concept of cluster design illustrated with worked examples, and introduce design effect that is a factor to inflate the standard sample size estimates.

• The Journal of Korean Medicine
• /
• v.25 no.1
• /
• pp.1-20
• /
• 2004

Objectives : Based on the analysis of Yin-Yang[음양] characteristics and symptoms, each person is classified into Yin-Yang. Also the validity of the result is statistically analized. Methods : From Feb. to May. 2003, the data were collected through a questionnaire given to 690 patients. The questionnaire was composed of 34 items which were about personality, habit, sweat, response to coldness, thirst, bowel, urine, physical shape, and menstruation for women only. SD(Semantic Differential Technique) used for each item, each item is measured as a contrast of two opposite symptoms. Reliability analysis was used to select items and categories. Based on means of items in each category the Yin-Yang index was developed. The validity of Yin-Yang index was investigated using classification and clustering analysis. In statistical analysis, SPSS V10.0.7 PC was used. Results : The obtained results are summarized as follows: 1) We constructed Yin-Yang index based on the middle point of the sum of categorical means. Then we classified each person into Yin or Yang. 2) To investigate the validity of the distribution of personal Yin-Yang degree, the crosstabulation of results from clustering and classification was used. The hit ratio for classification was much higher than Maximum Chance Criterion($C_{max}$), and concurrence in crosstabulation was successful. Therefore we can infer that the distribution of Yin-Yang was valid. Conclusions : Based on Yin-Yang characteristics and symptoms, we was analyzed personal degree of Yin-Yang, and confirmed the validity of its distribution. Therefore this index can be used further for Bian-Zheng [변증] and classification of the constitution.

• Journal of Service Research and Studies
• /
• v.2 no.2
• /
• pp.45-51
• /
• 2012

U-healthcare is real-time monitoring of personal biometric information using by portable devices, home network and information and communication technology based healthcare systems, and fused together automatically to overcome the constraints of time and space are connected with hospitals and doctors. As u-healthcare gives health service in anytime and anywhere, it becomes to be a new type of medical services in patients management and disease prevention. In this paper, recent changes in prevention-oriented care is analyzed in becoming early response for Healthcare Information System by requirements analysis for technology development trend. According to the healthcare system, PACS, OCS, EMR and emergency medical system, U-healthcare is presenting the design of a patient-centered integrated client system. As the relationship between the meaning of the terms is used in the ontology, information models in the system is providing a common vocabulary with various levels of formality. In this paper, we propose an ontology-based system for patient-centered services, including the concept of clustering to clustering the data to define the relationship between these ontologies for more systematic data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.12
• /
• pp.1618-1624
• /
• 2020

Intestinal obstruction diagnosis method based on X-ray can affect objective diagnosis because it includes subjective factors of the examiner. Therefore, in this paper, a detection method of Intestinal Obstruction from X-Ray image using Hough transform and PCM is proposed. The proposed method uses Hough transform to detect straight lines from the extracted ROI of the intestinal obstruction X-Ray image and bowel obstruction is extracted by using air fluid level's morphological characteristic detected by the straight lines. Then, ROI is quantized by applying PCM clustering algorithm to the extracted ROI. From the quantized ROI, cluster group that includes bowel obstruction's characteristic is selected and small bowel regions are extracted by using object search from the selected cluster group. The proposed method of using PCM is applied to 30 X-Ray images of intestinal obstruction patients and setting the initial cluster number of PCM to 4 showed excellent performance in detection and the TPR was 81.47%.

• Journal of Biosystems Engineering
• /
• v.33 no.5
• /
• pp.340-347
• /
• 2008

Dipstick urinalysis is used as a routine test for a screening test of UTI (urinary tract infection) in primary practice because urine dipstick test is simple. The result of dipstick urinalysis brings medical professionals to make a microscopic examination and urine culture for exact UTI diagnosis, therefore it is emphasized on a role of screening test. The objective of this study was to the classification between UTI patients and normal subjects using hybrid neural network classifier with enhanced clustering performance in urine dipstick screening test. In order to propose a classifier, we made a hybrid neural network which combines with RBF layer, summation & normalization layer and L VQ artificial neural network layer. For the demonstration of proposed hybrid neural network, we compared proposed classifier with various artificial neural networks such as back-propagation, RBFNN and PNN method. As a result, classification performance of proposed classifier was able to classify 95.81% of the normal subjects and 83.87% of the UTI patients, total average 90.72% according to validation dataset. The proposed classifier confirms better performance than other classifiers. Therefore the application of such a proposed classifier expect to utilize telemedicine to classify between UTI patients and normal subjects in the future.

• The korean journal of orthodontics
• /
• v.47 no.4
• /
• pp.248-255
• /
• 2017

Objective: The purpose of this study was to investigate whether the craniofacial patterns of Korean children with snoring and adenotonsillar hypertrophy (ATH) could be categorized into characteristic clusters according to age. Methods: We enrolled 236 children with snoring and ATH (age range, 5-12 years) in this study. They were subdivided into four age groups: 5-6, 7-8, 9-10, and 11-12 years. Based on cephalometric analysis, the sagittal and vertical skeletal patterns of each individual were divided into Class I, II, and III, as well as the normodivergent, hypodivergent, and hyperdivergent patterns, respectively. Cluster analysis was performed using cephalometric principal components in addition to the age factor. Results: Three heterogeneous clusters of craniofacial patterns were obtained in relation to age: cluster 1 (41.9%) included patients aged 5-8 years with a skeletal Class I or mild Class II and hyperdivergent pattern; cluster 2 (45.3%) included patients aged 9-12 years with a Class II and hyperdivergent pattern; and cluster 3 (12.8%) included patients aged 7-8 years with a Class III and hyperdivergent pattern. Conclusions: This study found that the craniofacial patterns of Korean children with snoring and ATH could be categorized into three characteristic clusters according to age groups. Although no significantly dominant sagittal skeletal discrepancy was observed, hyperdivergent vertical discrepancy was consistently evident in all clusters.

• The Korean Journal of Applied Statistics
• /
• v.18 no.1
• /
• pp.1-13
• /
• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• Health Policy and Management
• /
• v.32 no.4
• /
• pp.347-355
• /
• 2022

Background: Since November 2019, long-term care hospitals have been able to provide patients with discharging programs to support the elderly in the community. This study aimed to identify both patient- and hospital-level factors that affect successful community discharge from long-term care hospitals. Methods: A multilevel logistic regression model was performed using hospitals as a clustering unit. The dependent variable was whether a patient stayed in the community for at least 30 days after discharge from a long-term care hospital. As for the patient-level independent variables, an agreement between a patient and the family about discharge, length of hospital stay, patient category, and residence at discharge were included. The number of beds and the ratio of long-stay patients were selected for the hospital-level factors. The sample size was 1,428 patients enrolled in the discharging program from November 2019 to December 2020. Results: The number of patients who were discharged to the community and stayed at least for 30 days was 532 (37.3%). The intraclass correlation coefficient was 22.9%, indicating that hospital-level factors had a significant impact on successful community discharge. The odds ratio (OR) of successful community discharge increased by 1.842 times when the patients and their families agreed on discharge. The ORs also increased by 3.020 or 2.681 times, respectively when the patients planned to discharge to their own house or their child's house compared to those who didn't have a plan for residence at discharge. The ORs increased by 1.922 or 2.250 times when the hospitals were owned by corporate or private property compared to publicly owned hospitals. The ORs decreased by 0.602 or 0.520 times when the hospital was sized over 400 beds or located in small and medium-sized cities compared to less than 200 bedded hospitals or located in metropolitan cities. Conclusion: The results of the study showed that the patients' and their family's willingness for discharge had a great impact on successful community discharge and the hospital-level factors played a significant role in it. Therefore, it is important to acknowledge and support long-term care hospitals to involve active in the patient discharge planning process.

• The Korean Journal of Applied Statistics
• /
• v.33 no.6
• /
• pp.697-712
• /
• 2020

New infectious diseases have broken out repeatedly across the world over the last 20 years; COVID-19 is causing drastic changes and damage to daily lives. Furthermore, as there is no denying that new epidemics will appear in the future, there is a continuous need to develop measures aimed towards responding to economic damage. Against this backdrop, the living population is an important indicator that shows changes in citizens' life patterns. This study analyzes time-based and socio-environmental characteristics by detecting and classifying changes in everyday life caused by COVID-19 from the perspective of the floating population. k-shape Clustering is used to classify living population data of each of the 424 dong's in Seoul measured by the hour; then by applying intervention analysis and One-way ANOVA, each cluster's characteristics and aspects of change in the living population occurring in the aftermath of COVID-19 are scrutinized. In conclusion, this study confirms each cluster's obvious characteristics in changes of population flows before and after the confirmation of coronavirus patients and distinguishes groups that reacted sensitively to the intervention times on the basis of COVID-related incidents from those that did not.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.8
• /
• pp.3543-3549
• /
• 2015

Background: Bladder cancer is one of the most common cancers worldwide. Gene expression profiling using microarray technologies improves the understanding of cancer biology. The aim of this study was to determine the gene expression profile in Egyptian bladder cancer patients. Materials and Methods: Samples from 29 human bladder cancers and adjacent non-neoplastic tissues were analyzed by cDNA microarray, with hierarchical clustering and multidimensional analysis. Results: Five hundred and sixteen genes were differentially expressed of which SOS1, HDAC2, PLXNC1, GTSE1, ULK2, IRS2, ABCA12, TOP3A, HES1, and SRP68 genes were involved in 33 different pathways. The most frequently detected genes were: SOS1 in 20 different pathways; HDAC2 in 5 different pathways; IRS2 in 3 different pathways. There were 388 down-regulated genes. PLCB2 was involved in 11 different pathways, MDM2 in 9 pathways, FZD4 in 5 pathways, p15 and FGF12 in 4 pathways, POLE2 in 3 pathways, and MCM4 and POLR2E in 2 pathways. Thirty genes showed significant differences between transitional cell cancer (TCC) and squamous cell cancer (SCC) samples. Unsupervised cluster analysis of DNA microarray data revealed a clear distinction between low and high grade tumors. In addition 26 genes showed significant differences between low and high tumor stages, including fragile histidine triad, Ras and sialyltransferase 8 (alpha) and 16 showed significant differences between low and high tumor grades, like methionine adenosyl transferase II, beta. Conclusions: The present study identified some genes, that can be used as molecular biomarkers or target genes in Egyptian bladder cancer patients.