• Physical Therapy Rehabilitation Science
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• v.6 no.4
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• pp.176-181
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• 2017

Objective: The timed up and go (TUG) test is method used to determine the functional mobility of persons with stroke. Its reliability, validity, reaction rate, fall prediction, and psychological characteristics concerning ambulation ability have been validated. However, the relationship between TUG performance and community ambulation ability is unclear. The purpose of this study was to investigate whether the TUG performance time could indicate community ambulation levels (CAL) differentially in persons with chronic stroke. Design: Cross-sectional study. Methods: Eighty-seven stroke patients had participated in this study. Based on the self-reporting survey results on the difficulties experienced when walking outdoors, the subjects were divided into the independent community ambulation (ICA) group (n=35) and the dependent community ambulation group (n=52). Based on the area under the curve (AUC), the discrimination validity of the TUG performance time was calculated for classifying CAL. The Binomial Logistic Regression Model was utilized to produce the likelihood ratio of selected TUG cut-off values for the distinguishing of community ambulation ability. Results: The selected TUG cut-off values and the area under the curve were <14.87 seconds (AUC=0.871, 95% confidence interval=0.797-0.945), representing a mid-level accuracy. Concerning the likelihood ratio of the selected TUG cut-off value, it was found that the group with TUG performance times shorter than 14.87 seconds showed a 2.889 times higher probability of ICA than those with a TUG score of 14.87 seconds or longer (p<0.05). Conclusions: The TUG can be viewed as an assessment tool that is capable of classifying CAL.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Korean Journal of Community Nutrition
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• v.23 no.4
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• pp.277-288
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• 2018

Objectives: This study was performed to verify the validity and judgment criteria setting of a health status assessment tool based on dietary patterns for middle-aged women. Methods: A total of 474 middle-aged women who visited the Comprehensive Medical Examination Center at Hanmaeum Hospital in Changwon were enrolled (IRB 2013-0005). The validity was verified using clinical indicators for the diagnosis of metabolic syndrome (MS), and it was used to set the criteria for the tool. A logistic regression analysis was performed for validation. The area under-receiver operation (AUC), sensitivity, specificity, and Youden Index were calculated through ROC curve analysis. Statistical analysis was performed by SPSS 21, and p value <0.05 was considered to be statistically significant. Results: The mean score of the group with no MS (73.3 points) was significantly higher compared to the group with MS (65.7 points) (p<0.001). An analysis of the association between the tool scores and risk of MS showed a 0.15-fold reduction in the risk of MS every time the tool's score increased by one point. As the result of the ROC curve analysis, the assessment reference point was set to 71 points, indicating 77.0% sensitivity and 61.0% specificity. Risk of MS was significantly higher in the group with a score of less than 71.0 than a group with more than 71 points (OR=5.28, p<0.001). Conclusions: This study was the first attempt to develop a health status assessment tool based on the dietary patterns for middle-aged women, and this tool has proven its usefulness as an MS assessment tool through the application of middle-aged women in the field of health screening.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.22-24
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$, .734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$, .612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.928-936
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• 2013

This thesis is the fundamental study on the adaptation of escape device for reducing small fishes in set-net. The escape devices for experiments were made the mesh-type devices with three different mesh sizes (60.6, 75.8 and 120.0mm). The experiments of size selectivity on escape devices were conducted by using two kinds of species as black rockfish (Sebastes schlegeli) and sea perch (Lateolabrax maculatusi) in the experimental tank. The size selectivity curve was fitted by using a logistic function and the parameters of selectivity curve were estimated by a maximum likelihood method. In the results; 50% selection ranges for the mesh-type escape devices with three different mesh sizes were; a black rockfish was 18.99 in mesh size 60.6mm and 21.96 in mesh size 75.8mm (120mm could not estimate). A sea perch was 22.02 in mesh 60.6mm and 24.46 in mesh size 75.8mm (120mm could not estimate). The 50% selection range of a black rockfish was wilder than a sea perch about 1.1~.2 time. Therefore, the small fishes are able to reduce by using the mesh type escape device. However, the optimum mesh size should be decided to consider the size of target species and economics of catches.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.3
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• pp.188-199
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• 2013

This thesis is the fundamental study on the adaptation of escape device for reducing small size of fish in set-net. The escape devices for experiments were made the grid-type devices with three different slit sizes (15, 20 and 25mm). The experiments of size selectivity on escape devices were conducted by using two kinds of species as black rockfish (Sebastes schlegeli) and sea perch (Lateolabrax maculatusi) in the experimental tank. The size selectivity curve was fitted by using a logistic function and the parameters of selectivity curve were estimated by a maximum likelihood method. In the results; 50% selection ranges for the grid-type escape devices with three different slit sizes were; a black rockfish was 13.30, 19.22 and 22.06cm and a sea perch was 17.64, 20.91 and 22.78cm, respectively. The 50% selection range of a black rockfish was wilder than a sea perch about 1.1~1.3 time. Therefore, the small size of fish are able to reduce by using the grid type escape device. However, the optimum slit size of grid should be decided to consider the size of target species and economics of catches.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.190-193
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$,.734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$,.612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Journal of The Korean Society of Clinical Toxicology
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• v.18 no.2
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• pp.130-135
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• 2020

Purpose: Taking large amounts of glyphosate is life-threatening, but the amounts of glyphosate taken by patients for suicide are not known precisely. The purpose of this study was to find the predictors of large amounts of glyphosate ingestion. Methods: This retrospective study analyzed patients presenting to an emergency department with glyphosate intoxication between 2010 and 2019, in a single tertiary hospital. The variables associated with the intake amounts were investigated. The parameters were analyzed by multivariate variate logistic regression analyses and the receiver operating characteristic (ROC) curve. Results: Of the 28 patients with glyphosate intoxication, 15 (53.6%) were in the large amounts group. Univariate analysis showed that metabolic acidosis, lactic acid, and corrected QT (QTc) interval were significant factors. In contrast, multivariate analysis presented the QTc interval as the only independent factor with intoxication from large amounts of glyphosate. (odds ratio, 95% confidence interval: 1.073, 1.011-1.139; p=0.020) The area under the ROC curve of the QTc interval was 0.838. Conclusion: The QTc interval is associated significantly with patients who visit the emergency department after being intoxicated by large amounts of glyphosate. These conclusions will help in the initial triage of patients with glyphosate intoxication.

• Clinical and Experimental Pediatrics
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• v.62 no.8
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• pp.307-311
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• 2019

Purpose: Necrotizing enterocolitis (NEC) is one of the most serious complications of prematurity. Many risk factors can contribute to the development of NEC. The epidermal growth factor (EGF) plays a major role in intestinal barrier function, increases intestinal enzyme activity, and improves nutrient transport. The aim of this study was to assess the role of epidermal growth factor in the development of NEC in preterm neonates. Methods: In this study, 130 preterm neonates were included and divided into 3 groups, as follows: group 1, 40 preterm neonates with NEC; group 2, 50 preterm neonates with sepsis; and group 3, 40 healthy preterm neonates as controls. The NEC group was then subdivided into medical and surgical NEC subgroups. The serum EGF level was measured using enzyme-linked immunosorbent assay. Results: Serum EGF levels (pg/dL) were significantly lower in the NEC group (median [interquartile range, IQR], 9.6 [2-14]) than in the sepsis (10.1 [8-14]) and control groups (11.2 [8-14], P<0.001), with no significant difference between the sepsis and control groups, and were positively correlated with gestational age (r=0.7, P<0.001). A binary logistic regression test revealed that low EGF levels and gestational ages could significantly predict the development of NEC. The receiver-operating characteristic curve for EGF showed an optimal cutoff value of 8 pg/mL, with 73.3% sensitivity, 98% specificity, and an area under the curve of 0.92. Conclusion: The patients with NEC in this study had significantly lower serum EGF levels (P<0.001), which indicated that EGF could be a reliable marker of NEC in preterm neonates.

• Journal of Home Health Care Nursing
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• v.30 no.1
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• pp.48-58
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• 2023

Purpose: The purpose of this study was to construct a nomogram for predicting difficulty in peripheral intravenous cannulation (DPIVC) for adult inpatients. Methods: This study conducted a secondary analysis of data from the intravenous cannulation cohort by intravenous specialist nurses at a tertiary hospital in Seoul. Overall, 504 patients were included; of these, 166 (32.9%) patients with failed cannulation in the first intravenous cannulation attempt were included in the case group, while the remaining 338 patients were included in the control group. The nomogram was built with the identified risk factors using a multiple logistic regression analysis. The model performance was analyzed using the Hosmer-Lemeshow test, area under the curve (AUC), and calibration plot. Results: Five factors, including vein diameter, vein visibility, chronic kidney disease, diabetes, and chemotherapy, were risk factors of DPIVC. The nomogram showed good discrimination with an AUC of 0.81 (95% confidence interval: 0.80-0.82) by the sample data and 0.79 (95% confidence interval: 0.74-0.84) by bootstrapping validation. The Hosmer-Lemeshow goodness-of-fit test showed a p-value of 0.694, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of DPIVC. Conclusion: This nomogram can be used in clinical practice by nurses to predict DPIVC probability. Future studies are required, including those on factors possibly affecting intravenous cannulation.