• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.436-448
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• 2020

This study aims to determine the effect of e-cigarettes on oral health by investigating the association between the use of different tobacco products and oral health among Korean adults aged 19 years and older. Data from the 2017 Community Health Survey were used for the study. Respondents were divided into four groups: non-smokers, cigarette smokers, e-cigarette smokers, and users of both products. A sample of 228,357 respondents was selected for analysis. Twenty-four questionnaires with missing values (non-response or refusal) were excluded from the sample. A regression analysis was performed with oral as the dependent variable. A multivariate regression analysis showed a significant difference between cigarette smokers and users of both products when compared to the non-smokers. However, e-cigarette users showed a significant when the variables were correlated with age and gender. There was no significant difference in other dependent variables in a multivariate regression analysis. The results of the study indicated no association between e-cigarette use and oral health. More research is needed on factors such as amount and intensity of e-cigarette use.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

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

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.177-185
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• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.267-275
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• 2011

According to recent studies, Bayesian information criteria(BIC) is proposed to determine the structural dimension of the central subspace through sliced inverse regression(SIR) with high-dimensional predictors. The BIC may be useful in K-means clustering inverse regression(KIR) with high-dimensional predictors. However, the direct application of the BIC to KIR may be problematic, because the slicing scheme in SIR is not the same as that of KIR. In this paper, we present empirical penalty term studies of BIC in KIR to identify the most appropriate one. Numerical studies and real data analysis are presented.

• Analytical Science and Technology
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• v.37 no.1
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• pp.12-24
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• 2024

In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of rice polishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains were analyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Their correlation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensities showed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based on those lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracy performances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model using Si I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy (2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, which is closed related to nutrition, shelf life, appearance, and commercial value of rice products.

• Journal of Yeungnam Medical Science
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• v.36 no.2
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• pp.141-147
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• 2019

Background: The present study aimed to evaluate the clinical association between cadmium exposure and hearing impairment among the Korean population. Methods: This retrospective cross-sectional study used the data obtained from the Korean National Health and Nutrition Examination Survey were used for our study. Finally, 3,228 participants were included in our study, which were then divided into quartiles based on their blood cadmium levels: first quartile (1Q), second quartile (2Q), third quartile (3Q), and fourth quartile (4Q) groups. The hearing thresholds were measured using an automatic audiometer at 0.5, 1, 2, 3, 4, and 6 kHz. Hearing loss (HL) was defined as >25 dB average hearing threshold (AHT). Results: All the groups had 807 participants each. The area under the receiver operating characteristic curves of cadmium level for HL were 0.634 (95% confidence interval [CI], 0.621-0.646). The participants in the 4Q group had higher Low/Mid-Freq, High-Freq, and AHT values than those in the other groups in the multivariate analysis after adjusting for confounding factors. The logistic regression showed that the OR for HL per $1{\mu}g/L$ increase in cadmium was 1.25 (95% CI, 1.09-1.44; p=0.002) on the multivariate analysis. Moreover, the multivariate logistic regression analyses revealed that the participants in the 4Q group exhibited a 1.59-, 1.38-, and 1.41-fold higher odds for HL than those in the 1Q, 2Q, and 3Q groups, respectively. Conclusion: High cadmium level quartile was associated with increased hearing thresholds and HL among the Korean adult population.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Journal of Korean Neurosurgical Society
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• v.59 no.4
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• pp.368-373
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• 2016

Objective : Delayed hinge fracture (HF) that develops after cervical open door laminoplasty can be a source of postoperative complications such as axial pain. However, risk factors related to this complication remain unclear. We performed a retrospective clinical series to determine risk factors for delayed HF following plate-only open-door cervical laminoplasty. Methods : Patients who underwent plate-only open-door laminoplasty and had available postoperative computed tomography (CT) scans (80 patients with 270 laminae) were enrolled. Hinge status, hinge gutter location, open location, hinge width, number of screws used, operation level, and open angle were observed in the CT to determine radiographic outcome. Demographic data were collected as well. Radiographic and clinical parameters were analyzed using univariate and multivariate logistic regression analysis to determine the risk factors for HF. Results : Univariate logistic regression analysis results indicated poor initial hinge status, medially placed hinge gutter, double screw fixation on the elevated lamina, upper surgical level, and wide open angle as predictors for HF (p<0.05). Initial hinge status seemed to be the most powerful risk factor for HF (p=0.000) and thus was collinear with other variables. Therefore, multivariate logistic regression analysis was performed excluding initial hinge status, and the results indicated that medially placed hinge gutter, double screw fixation on the elevated lamina, and upper surgical level were risk factors for HF after adjustment for other confounding factors. Conclusion : To prevent HF and to draw a successful postoperative outcome after cervical laminoplasty, surgical and clinical precautions should be considered.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.