• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.363-364
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• 2023

The tunnel construction projects is demanded more efficient risk management measures and loss forecasts to prepare for risk losses from an increase in the trend of tunnel construction. This study aims to analyze the risk factors that caused the loss of material in actual tunnel construction and to develop a quantified predictive loss model, based on the past loss record of tunnel construction projects.

• Health Policy and Management
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• v.27 no.2
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• pp.149-156
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• 2017

Background: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. Methods: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures: model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication-days plus model 3). We evaluated model performance using $R^2$ at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. Results: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. $R^2$ values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding $R^2$ values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. Conclusion: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.

• Journal of Korean Academy of Nursing
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• v.41 no.3
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• pp.423-431
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• 2011

Purpose: The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers. Methods: Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and .II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method. Results: Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR. Conclusion: Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

• Journal of East-West Nursing Research
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• v.29 no.1
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• pp.68-77
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• 2023

Purpose: The purposes of this study were to develop a predictive model and evaluate this model of turnover in hospital nurses. Methods: Participants were 1,565 nurses from a tertiary hospital in South Korea. Descriptive statistics and a decision-tree analysis were performed using the SPSS WIN 23.0 program. Results: The turnover groups were presented in eleven different pathways by decision tree analysis. There were three high-risk groups with a higher turnover rate than the average, and eight low-risk groups with a lower turnover rate. Among them, two low-risk groups had a 0% turnover rate. The groups were classified according to general characteristics such as position, period of temporary position, clinical career at last working unit, total clinical career, and period of leave of absence. The accuracy of the model was 83.2%, sensitivity 63.7%, and specificity 98.1%. Conclusion: This predictive model of turnover may be used to screen the turnover risk groups and contribute for decreasing the turnover of hospital nurses in South Korea.

• Journal of Environmental Health Sciences
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• v.19 no.3
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• pp.46-51
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• 1993

In order to survery the risk of air-borne lead to human, the relation between air-borne lead level and blood lead level was examined by using of the kinetic model and statistical model. The results of this survey were as follows: 1. The pathways of lead intake were food and water, mainly. 2. Though blood lead level of Korean urbanire was higher than that of American or Japanese, it was not so severe as to influence human health. 3. The lead content in food and water was high, and so it is needed to confirm the cause of high content was whether second contamination by air pollution or not.

• Physical Therapy Korea
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• v.27 no.2
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• pp.140-148
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• 2020

Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce. Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method. Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined. Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04). Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

• Journal of the Korean Dietetic Association
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• v.25 no.1
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• pp.30-43
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• 2019

This study compared the predictive models for the growth kinetics of Staphylococcus aureus in ham rice balls. In addition, a semi-quantitative risk assessment of S. aureus on ham rice balls was conducted using FDA-iRISK 4.0. The rice was rounded with chopped ham, which was mixed with mayonnaise (SHM), soy sauce (SHS), or gochujang (SHG), and was contaminated artificially with approximately $2.5{\log}\;CFU{\cdot}g^{-1}$ of S. aureus. The inoculated rice balls were then stored at $7^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$, and the number of viable S. aureus was counted. The lag phases duration (LPD) and maximum specific growth rate (SGR) were calculated using a Baranyi model as a primary model. The growth parameters were analyzed using the polynomial equation as a function of temperature. The LPD values of S. aureus decreased with increasing temperature in SHS and SHG. On the other hand, those in SHM did not show any trend with increasing temperature. The SGR positively correlated with temperature. Equations for LPD and SGR were developed and validated using $R^2$ values, which ranged from 0.9929 to 0.9999. In addition, the total DALYs (disability adjusted life years) per year in the ham rice balls with soy sauce and gochujang was greater than mayonnaise. These results could be used to calculate the expected number of illnesses, and set the hazard management method taking the DALY value for public health into account.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.3
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• pp.307-314
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• 2016

Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models.

• Food Science of Animal Resources
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• v.37 no.4
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• pp.579-592
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• 2017

This study assessed the quantitative microbial risk of non-enterohemorrhagic Escherichia coli (EHEC). For hazard identification, hazards of non-EHEC E. coli in natural and processed cheeses were identified by research papers. Regarding exposure assessment, non-EHEC E. coli cell counts in cheese were enumerated, and the developed predictive models were used to describe the fates of non-EHEC E. coli strains in cheese during distribution and storage. In addition, data on the amounts and frequency of cheese consumption were collected from the research report of the Ministry of Food and Drug Safety. For hazard characterization, a doseresponse model for non-EHEC E. coli was used. Using the collected data, simulation models were constructed, using software @RISK to calculate the risk of illness per person per day. Non-EHEC E. coli cells in natural- (n=90) and processed-cheese samples (n=308) from factories and markets were not detected. Thus, we estimated the initial levels of contamination by Uniform distribution ${\times}$ Beta distribution, and the levels were -2.35 and -2.73 Log CFU/g for natural and processed cheese, respectively. The proposed predictive models described properly the fates of non-EHEC E. coli during distribution and storage of cheese. For hazard characterization, we used the Beta-Poisson model (${\alpha}=2.21{\times}10^{-1}$, $N_{50}=6.85{\times}10^7$). The results of risk characterization for non-EHEC E. coli in natural and processed cheese were $1.36{\times}10^{-7}$ and $2.12{\times}10^{-10}$ (the mean probability of illness per person per day), respectively. These results indicate that the risk of non-EHEC E. coli foodborne illness can be considered low in present conditions.