• Journal of Digital Convergence
• /
• v.13 no.1
• /
• pp.353-366
• /
• 2015

The purpose of this study was to examine how region-specific characteristics affect the occurrence of cardiac arrest. To analyze, we combined a unique data set including key indicators of health condition and cardiac arrest occurrence at the 244 small administrative districts. Our data came from two main sources in Korea Center For Disease Control and Prevention (KCDC): 2010 Out-of-Hospital Cardiac Arrest Surveillance and Community Health Survey. We analyzed data by using multiple regression, geographically weighted regression and decision tree. Decision tree model is selected as the final model to explain regional variations of cardiac arrest. Factors of regional variations of cardiac arrest occurrence are population density, diagnosis rates of hypertension, stress level, participating screening level, high drinking rate, and smoking rate. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors are important when regionally customized health policy is implemented to decrease the cardiac arrest occurrence.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.425-436
• /
• 2015

This study is to investigate how the expenditures for hypertension is affected by socioeconomic, health care resources, and health behavior factors with a special emphasis on geographic variations and to provide the data about regional management for hypertension. To analyze, we combined a unique data set including key indicators from Medical Service Usage Statistics 2012 by Region by National Health Insurance Corporation, Annual Community Health Survey 2012 by Korea Centers for Disease Control and Prevention and other government organizations at the 247 small administrative districts. We found that the average expenditures of hypertension in 249 small districts is 62,000 won and coefficient of variation is 30.0. Major factors of differences in hypertension expenditure is population density, marital status, household income, number of hospital per 100 thousand, medical expenses outside the jurisdiction, drinking rate, moderate and over-intensity physical activity, and hypertension diagnosis rate. The results of decision tree was that there were significant differences between regions in hypertension diagnosis rate, household income, marital status, number of hospital per 100 thousand, obesity rate, drinking rate. This study concluded that determinants of geographic variations in hypertension spending are not only health resources and socioepidemic characteristics but health behaviors.

• Journal of Digital Convergence
• /
• v.13 no.12
• /
• pp.245-257
• /
• 2015

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

• The Korean Journal of Applied Statistics
• /
• v.22 no.4
• /
• pp.771-780
• /
• 2009

Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.1
• /
• pp.97-101
• /
• 2012

Objective: With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction. Methods: A retrospective design was adopted to collect the consecutive data about patients aged 60 or over with a gastric cancer diagnosis after surgery in an adult intensive care unit in a medical university hospital in Shenyang, China, from January 2010 to March 2011. Characteristics of patients and the length their ICU stay were gathered for analysis by univariate and multivariate Cox regression to examine the relationship with potential candidate factors. A regression tree was constructed to predict the length of ICU stay and explore the important indicators. Results: Multivariate Cox analysis found that shock and nutrition support need were statistically significant risk factors for prolonged length of ICU stay. Altogether, eight variables entered the regression model, including age, APACHE II score, SOFA score, shock, respiratory system dysfunction, circulation system dysfunction, diabetes and nutrition support need. The regression tree indicated comorbidity of two or more kinds of shock as the most important factor for prolonged length of ICU stay in the studied sample. Conclusions: Comorbidity of two or more kinds of shock is the most important factor of length of ICU stay in the studied sample. Since there are differences of ICU patient characteristics between wards and hospitals, consideration of the data-mining technique should be given by the intensivists as a length of ICU stay prediction tool.

• The Korean Journal of Health Service Management
• /
• v.10 no.3
• /
• pp.63-74
• /
• 2016

Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.

• Journal of Korean Society of Forest Science
• /
• v.50 no.1
• /
• pp.25-28
• /
• 1980

The purpose of the this study was to compare the torlent tree to drought among the Pinus koraiensis, Abies holophylla, and Ginkgo biloba. The water relations parameters of leafy shoots were measured by the Pressure chamber technique from August 10 to September 4 in 1980. On the water relations parameters such as original osmotic pressure(${\pi}_0$), osmotic pressure(${\pi}_p$) and relative water content (RWC) at incipient plasmolysis, and Vp/Vo ratio of the volume of osmotic water (Vp) at incipient plasmolysis to total symplasmic water (Vo) basis, the Abies holophylla shoots showed ontogenetically a superior osmoregulation which are closely associated with drought resistance compared with Pinus koraiensis, and the Ginkgo biloba shoots showed the heighest among these three species.

• Journal of the Korean Society of Radiology
• /
• v.13 no.7
• /
• pp.941-946
• /
• 2019

Computed tomography (CT) has been increasing in frequency and indications for use in clinical diagnosis and treatment decisions. Multidetector CT has the advantage of shortening the inspection time and obtaining a high resolution image compared to a single detector CT, but has been pointed out the disadvantage of increasing the radiation exposure. In addition, when the low tube voltage is used to reduce the exposure dose in the CT, noise increases relatively. In the existing method, the method of finding the optimal image quality using the method of adjusting the parameters of the image reconstruction method is not a fundamental measure. In this study, we applied a double-tree complex wavelet algorithm and analyzed the results to maintain the normal signal and remove only noise. Experimental results show that the noise is reduced from 8.53 to 4.51 when using a complex oriented 2D method with 100kVp and 0.5sec rotation time. Through this study, it was possible to remove the noise and reduce the patient dose by using the optimal noise reduction algorithm. The results of this study can be used to reduce the exposure of patients due to the low dose of CT.

• Asian Oncology Nursing
• /
• v.5 no.1
• /
• pp.3-10
• /
• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.23 no.5
• /
• pp.964-968
• /
• 2009

The purpose of this study is to select the major pulse indicators and evaluate their significance in discriminating the subtypes of Pattern Identifications (PI) from stroke patients. Decision tree analysis was carried out using clinical data collected from 835 stroke patients with the same subtypes diagnosed identically by two experts with more than 3 year clinical experiences. Among the 10 pulse indicators, 6 major pulse indicators (slow, rapid, strong, weak, slippery, and fine pulse) were selected by decision tree analysis. The accumulated distributions of six pulse indicators in each PI showed that strong was major pulse indicator in Fire-Heat pattern, slippery in Dampness Phlegm pattern, weak in Qi Deficiency pattern. But there were two major combinations in Yin deficiency pattern, weak or fine with rapid pulse and weak or fine without rapid pulse. Therefore, it is suggested that 6 pulse indicators can be used for discrimination of PI in stroke patients, though the combination studies between these pulse indicators and the other PI indicators are left for further study.