• Journal of Digital Convergence
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• v.13 no.2
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• pp.215-225
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• 2015

This study was conducted to develop a severity-adjusted LOS(Length of Stay) model for knee replacement patients and identify factors that can influence the LOS by using the Korean National Hospital Discharge in-depth Injury Survey data. The comorbidity scoring systems and data-mining methods were used to design a severity-adjusted LOS model which covered 4,102 knee replacement patients. In this study, a decision tree model using CCS comorbidity scoring index was chosen for the final model that produced superior results. Factors such as presence of arthritis, patient sex and admission route etc. influenced patient length of stay. And there was a statistically significant difference between real LOS and adjusted LOS resulted from health-insurance type, bed size, and hospital location. Therefore the policy alternative on excessive medical utilization is needed to reduce variation in length of hospital stay in patients who undergo knee replacement.

• Quality Improvement in Health Care
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• v.10 no.2
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• pp.216-231
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• 2003

Objectives: To develop a model that predicts a death probability of acute myocardial infarction(AMI) patient, and to evaluate a performance of hospital services using the developed model. Methods: Medical records of 861 AMI patients in 7 general hospitals during 1996 and 1997 were reviewed by two trained nurses. Variables studied were risk factors which were measured in terms of severity measures. A risk model was developed by using the logistic regression, and its performance was evaluated using cross-validation and bootstrap techniques. The statistical prediction capability of the model was assessed by using c-statistic, $R^2$ as well as Hosmer-Lemeshow statistic. The model performance was also evaluated using severity-adjusted mortalities of hospitals. Results: Variables included in the model building are age, sex, ejection fraction, systolic BP, congestive heart failure at admission, cardiac arrest, EKG ischemia, arrhythmia, left anterior descending artery occlusion, verbal response within 48 hours after admission, acute neurological change within 48 hours after admission, and 3 interaction terms. The c statistics and $R^2$ were 0.887 and 0.2676. The Hosmer-Lemeshow statistic was 6.3355 (p-value=0.6067). Among 7 hospitals evaluated by the model, two hospitals showed significantly higher mortality rates, while other two hospitals had significantly lower mortality rates, than the average mortality rate of all hospitals. The remaining hospitals did not show any significant difference. Conclusion: The comparison of the qualities of hospital service using risk-adjusted mortality rates indicated significant difference among them. We therefore conclude that risk-adjusted mortality rate of AMI patients can be used as an indicator for evaluating hospital performance in Korea.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.89-99
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• 2015

This study aims to develop a LOS(Length of Stay) bench-making system that can provide efficient by comparing the LOS management of other hospital and level evaluation for inducing the LOS to manage their own activities. The convergence LOS bench-making web program has been implemented to compare a variety of beds, regional group, followed reporting with excel files downloads by using the severity-adjusted LOS model of Korean National Hospital Discharge in-depth Injury Survey data. Features that are computed in real-time severity-adjusted LOS was also implemented. Trial operating results, bench-making system was confirmed efficient for management of LOS on the long-term care and group of disease in hospital from the staff or medical department, receive requests comparative statistics by area and disease group. Therefore the policy alternative on extension of severity-adjusted LOS is needed to utilized bench-making system on LOS.

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

• Journal of Yeungnam Medical Science
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• v.40 no.2
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• pp.146-155
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• 2023

Background: Despite the recent increasing trend in the prevalence of type 2 diabetes among older individuals, the relationship between diabetic retinopathy (DR) and chronic kidney disease (CKD) in these patients remains unclear. This study investigated the severity of renal dysfunction according to the degree of DR in older patients with type 2 diabetes. Methods: A total of 116 patients with diabetes and CKD stage ≥3 who visited both the nephrology and ophthalmology outpatient departments between July 2021 and January 2022 were screened. There were 53 patients in the no DR group, 20 in the nonproliferative DR (NPDR) group, and 43 in the proliferative DR (PDR) group. Results: DR severity was related to the deterioration of renal function. The proportion of patients with advanced CKD significantly increased with DR severity (p for trend <0.001). In the multivariate regression model adjusted for age of ≥80 years, male sex, poorly controlled diabetes, macroalbuminuria, insulin use, diabetes duration of ≥10 years, cerebrovascular accident, hypertension, hyperlipidemia, and cardiovascular disease history, the odds ratio compared with the no DR group was approximately 4.6 for the NPDR group and approximately 11.8 for the PDR group, which were both statistically significant (p=0.025 and p<0.001, respectively). Conclusion: DR severity in older patients with diabetes may be associated with deterioration of renal function and high prevalence of advanced CKD. Therefore, periodic examination for DR in older patients with diabetes is important for predicting renal function deterioration and CKD progression.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.8 no.3
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• pp.302-313
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• 2001

Purpose: To identify characteristics of chronic fatigue, difference between the duration of fatigue and characteristics of chronic fatigue, and association between chronic fatigue and related factors in adults. Method: The subjects for this study were 180 adults who had experienced fatigue for over one month The measurement tools were the Revised Fatigue Scale by Chalder et al. (1993), the Visual Analogue Scale-energy developed by Lee et al. (1991), the BEPSI (Brief Encounter Psychosocial Instrument) by Frank & Zyzanski (1988), Zung's self rating depression scale (1965) and life style items including exercise sleep, drinking smoking and diet as developed by the researchers. Results : Those who complained of fatigue over six months experienced higher disturbances in their daily life compared to those who had suffered from fatigue less than six months. There were a significant correlation between severity of fatigue and depression (r=.46, p<.001), stress (r=.41, p<.001), and sleep (r=.20, p<.01) Statistically significant relationships were found between severity of fatigue and amount of exercise per week (F=3.79. p<.05) disturbed sleep (t=-2.66, p<.01), number of times awakened during the night (F=3.48, p<.05) types of drinking (F=2.65, p<.05), and diet regularity (F=5.83, p<.01). The construction of a multiple regression model revealed an adjusted $R^2$ of .27 with the depression score serving the major predictor variables for severity of fatigue. Men and people in the younger age group were more energetic than women and those in other age groups. Also married people experienced more fatigue than people who were single. divorced or separated. Conclusion: In nursing interventions for fatigue, medical personnel should consider sociodemographic characteristics of the clients, ways to reduce of stress and counter disturbances in daily life and develop strategies for a health promoting life style.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.608-613
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• 2003

In this paper, the variability of modal properties caused by temperature effects is assessed to adjust modal data used for frequency-based damage detection in plate-girder bridges. First, experiments on model plate-girder bridges are described. Next, the relationship between temperature and natural frequencies is assessed and a set of empirical frequency-correction formula are analyzed for the test structure. Finally, a frequency-eased method is used to locate and estimate severity of damage in the test structure using experimental modal data which are adjusted by the frequency-correction formula. Here, local damage in beam-type structures is detected by using measured frequencies and analytical mode shapes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.535-540
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• 2003

The objective of this paper is to assess the variability of modal properties caused by temperature effects and to adjust modal data used for frequency-based damage detection in plate-girder bridges. First, experiments on model plate-girder bridges are described. Next, the relationship between temperature and natural frequencies is assessed and a set of empirical frequency-correction formula are analyzed for the test structure. Finally, a frequency-based method is used to locate and estimate severity of damage in the test structure using experimental modal data which are adjusted by the frequency-correction formula. Here, local damage in beam-type structures is detected by using measured frequencies and analytical mode shapes.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.379-384
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• 2011

The scope of the problems that could be solved by monitoring and the improvement of the recognition time is directly correlated to the performance of the management function of the business process. However, the current monitoring process of business activities decides whether to apply warnings or not by assuming a fixed environment and showing expressions based on the design rules. Also, warnings are applied by carrying out the measuring process when the event attribute values are inserted at every point. Therefore, there is a limit for distinguishing the range of occurrence and the level of severity in regard to the new external problems occurring in a complicated environment. Such problems cannot be ed. Also, since it is difficult to expand the range of problems which can be possibly evaluated, it is impossible to evaluate any unexpected situation which could occur in the execution period. In this paper, a process-evaluating model based on the goal scenario is suggested to provide constant services through the current monitoring process in regard to the service demands of the new scenario which occurs outside. The new demands based on the outside situation are analyzed according to the goal scenario for the process activities. Also, by using the meta-heuristic algorithm, a similar process model is found and identified by combining similarity and interrelationship. The process can be stopped in advance or adjusted to the wanted direction.