• Health Policy and Management
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• v.16 no.4
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• pp.128-146
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• 2006

The objective of this paper is to examine the proportion and characteristics of non-emergent patients at emergency departments. The observational survey was conducted using a structured form used by emergency medicine specialists or senior residents on June 7-20, 2005. 1,526 patients at ten emergency centers took part in this study. The structural form contained type of insurance, route and means of emergency department (ED) visit, triage based on the Manchester Triage Scale(MTS)-modified criteria, emergency level based on the government defined rule, type of emergency centers (Regional Emergency Medical Center; REMC, Local Emergency Medical Center; LEMC, Local Emergency Agency; LEA), as well as patient's general information. Data were analyzed using SAS statistical program(V.8.2). Descriptive analysis was performed to describe the magnitude of non-emergent patients. ${\chi}^2-analysis$ and logistic regression analysis was performed to identify the nonurgent patients' characteristics. In the MTS-modified criteria, we found a 15.3% rate of non-emergent patients. This rate differed from that of non-emergent patients obtained using government's rule. In particular, there were inaccuracies in the definition of government rule on non-emergent patients, so it is necessary to apply the new government rule regarding classification of non-emergent patients. There were significant differences in the rate of non-emergent patients according to type of ED, means of ED visit, time to visit, and insurance. Non-emergent patients are more likely to visit a D-type ED(LEA having less than 20,000 patients annually), not to use ambulance, to have 'Automobile Insurance, Industrial Accident Compensation Insurance, or pay out-of-pocket'. Non-emergent patients tend to visit ED due to illness rather than injury. Further studies on the development' of triage scale and reexamination of the government's rule on emergency visits are required for future policy in this area.

• Journal of Gastric Cancer
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• v.15 no.4
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• pp.262-269
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• 2015

Purpose: Intraabdominal abscess is one of the most common reasons for re-hospitalization after gastrectomy. This study aimed to develop a model for estimating the probability of intraabdominal abscesses that can be used during the postoperative period. Materials and Methods: We retrospectively reviewed the clinicopathological data of 1,564 patients who underwent gastrectomy for gastric cancer between 2010 and 2012. Twenty-six related markers were analyzed, and multivariate logistic regression analysis was used to develop the probability estimation model for intraabdominal abscess. Internal validation using a bootstrap approach was employed to correct for bias, and the model was then validated using an independent dataset comprising of patients who underwent gastrectomy between January 2008 and March 2010. Discrimination and calibration abilities were checked in both datasets. Results: The incidence of intraabdominal abscess in the development set was 7.80% (122/1,564). The surgical approach, operating time, pathologic N classification, body temperature, white blood cell count, C-reactive protein level, glucose level, and change in the hemoglobin level were significant predictors of intraabdominal abscess in the multivariate analysis. The probability estimation model that was developed on the basis of these results showed good discrimination and calibration abilities (concordance index=0.828, Hosmer-Lemeshow chi-statistic P=0.274). Finally, we combined both datasets to produce a nomogram that estimates the probability of intraabdominal abscess. Conclusions: This nomogram can be useful for identifying patients at a high risk of intraabdominal abscess. Patients at a high risk may benefit from further evaluation or treatment before discharge.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.143-151
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• 2006

This paper deals with the simulation model for design and operations of A-Mart logistic center for low temperature products. In developed simulation model, receiving docks, digital classification system (DCS), shipping docks, material handling devices and manual sorting stations are considered. Five types of cargo such as fruits, palletized fruits, delicatessen and fisheries, vegetables and refrigerated cargoes are considered. The simulation model and process animation are developed using the simulation package ARENA. Among various design and operation alternatives consisting of the number of workers of receiving dock, allocation of receiving docks by cargo types, DCS capacity, the number of folk-lift, the number of manual sorting operators and overall layout, the best alternatives of each subsystem are selected by simulation analysis. The major performance measures such as DCS throughput, utilization of operators at each station, receiving docks utilization and folk-lift utilization are considered for the alternative evaluation.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.57-64
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.

• Korea Journal of Hospital Management
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• v.25 no.3
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• pp.29-37
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• 2020

Purposes: The purpose of this study was to identify the factors related to the long-stay hospitalization of dementia patients aged 65 years or older who had received inpatient care at geriatric hospitals according to the minute facility characteristics and patient features. Methodology: This study was conducted on 317,353 cases of 1,512 geriatric hospitals using the Health Insurance Review and Assessment Service dataset. The data collected were processed using the SAS Enterprise Guide 4.3 for descriptive statistics, the chi-square test, and the binary logistic regression analysis. Findings: As a result of the study, in the facility characteristics of geriatric hospitals, the long-stay hospitalization of the aged with dementia were found to be related to the type of facility establishment, the number of hospital beds, the number of medical specialists, the number of nursing personnel, and the number of geriatric hospitals by region and province. In the personal features of patients, the long-stay hospitalization was found to be associated with the gender, age, insurance, and the patient classification groups. Practical Implication: Considering the results of this study, it seems that securing the sufficient medical personnel in a geriatric facility, providing the good quality medical services, and preparing the appropriate discharge plan can reduce the unnecessary long-stay hospitalization and spend the medical expenses for the older patients.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.

• Child Health Nursing Research
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• v.18 no.3
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• pp.143-149
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• 2012

Purpose: The aim was to explore the relationship between health status/life style and prevalence of atopic dermatitis in adolescents. Methods: Data from 1,743 adolescents (aged 13 to 18 years) derived from the fourth Korea National Health and Nutrition Examination Survey (KNHANES) were analyzed using binary logistic regression and the classification and regression tree (CART) model. Results: The prevalence rate of atopic dermatitis was 10.3%. Third quartile income class (OR=2.19, 95% CI=1.20-4.00), fair and unhealthy self-rated health (OR=1.49, 95% CI=1.06-2.11; OR= 2.73, 95% CI=1.64-4.55), past or current smoker (OR=0.41, 95% CI=0.20-0.85; OR=0.44, 95% CI=0.21-0.92), and more than average stress (OR=1.43, 95% CI=1.03-1.99) were related with increased prevalence of atopic dermatitis. The high prevalence risk groups for atopic dermatitis were adolescents who perceived themselves to be healthy on self-rated health and had more than average stress and those who perceived themselves to be unhealthy and who never smoked. Conclusion: The results of the study indicate that health status, smoking, and stress are associated with a prevalence risk for adolescent atopic dermatitis in different degrees. Therefore, for control of atopic dermatitis, various interventions for smoking and stress need to be reinforced for the high prevalence atopic dermatitis groups.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.241-250
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• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.54-60
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• 2018

Objective: Persons with chronic stroke fall more often than healthy elderly individuals. The Timed Up and Go test (TUG) is used as a fall prediction tool, but only provides a result for the total measurement time. This study aimed to determine the optimal cut-off values for each of the 6 components of the TUG. Design: Retrospective study. Methods: Thirty persons with chronic stroke participated in the study. TUG evaluation was performed using a wearable miniaturized inertial sensor. Sensitivity, specificity, and predictive values were calculated using the Receiver Operating Characteristic (ROC) curve analysis for the measured values in each section. Optimal values for fall risk classification were determined. Logistic regression analysis was used to investigate the risk of future falls based on TUG. Results: The cut-off values of the 6 sections of the TUG were determined, as follows: sit-to-stand >2.00 seconds (p<0.05), forward gait >4.68 seconds (p<0.05), mid-turn >3.82 seconds (p<0.05), return gait >4.81 seconds (p<0.05), end-turn >2.95 seconds (p<0.05), and stand-to-sit >2.13 seconds (p<0.05). The risk of falling increased by 2.278 times when the mid-turn value was >3.82 seconds (p<0.05). Conclusions: The risk of falls increased by 2.28 times when the value of the mid-turn interval exceeded 3.82 seconds. Therefore, when interpreting TUG results, the predictive accuracy for falls will be higher when the measurement time for each section is analyzed, together with the total time for TUG.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.31 no.3
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• pp.237-254
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• 2021

Objectives: This study aims to classify the latent profiles in the exposure to hazard factors of salaried employees and test the determinants. Methods: Latent profile analysis(LPA) was conducted using data from the fifth Korean Working Conditions Survey(KWCS). 30,050 of salaried employees were the subjects of this study. After classifying the employees, multinomial logistic regression was used to test the determinants. Results: Salaried employees were classified with three latent profiles based on the exposure to the hazard factors. Employees included in class 1(32.8%) tend to experience low level of physical hazard factors, moderate level of psychological hazard factors, and high level of office work hazard factors. Employees included in class 2(61.8%) tend to be exposed to the moderate to high level of physical hazard factors, moderate to low level of psychological hazard factors, and low level of office work hazard factors. Employees included in class 3(5.4%) tend to experience high level of psychological and physical hazard factors, and moderate level of office work hazard factors. After classification, the demographic, health-, and employment-related variables were tested. Conclusions: This study clarified the features of each class, and proved that employees in class 3 are quite hazardous in that they are exposed to physical and psychological hazard factors much more frequently than other employees. Thus, this study can be used in predicting the high-risk employees and taking preemptive measures for preventing industrial accidents.