• Economic and Environmental Geology
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• v.55 no.2
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• pp.127-135
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• 2022

Excessive presence of As level in groundwater is a major health problem worldwide. In the Red River Delta in Vietnam, several million residents possess a high risk of chronic As poisoning. The As releases into groundwater caused by natural process through microbially-driven reductive dissolution of Fe (III) oxides. It has been extracted by Red River residents using private tube wells for drinking and daily purposes because of their unawareness of the contamination. This long-term consumption of As-contaminated groundwater could lead to various health problems. Therefore, a predictive model would be useful to expose contamination risks of the wells in the Red River Delta Vietnam area. This study used four machine learning algorithms to predict the As probability of study sites in Red River Delta, Vietnam. The GBM was the best performing model with the accuracy, precision, sensitivity, and specificity of 98.7%, 100%, 95.2%, and 100%, respectively. In addition, it resulted the highest AUC of 92% and 96% for the PRC and ROC curves, with Eh and Fe as the most important variables. The partial dependence plot of As concentration on the model parameters showed that the probability of high level of As is related to the low number of wells' depth, Eh, and SO₄, along with high PO₄^3- and NH₄⁺. This condition triggers the reductive dissolution of iron phases, thus releasing As into groundwater.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.323-331
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• 2021

Purpose: To investigate the diagnostic criteria of T1-weighted imaging (T1W) and time-of-flight (TOF) imaging for detecting intraplaque hemorrhage (IPH) of a vertebrobasilar artery (VBA) compared with simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) imaging. Materials and Methods: Eighty-seven patients with VBA atherosclerosis who underwent high resolution MR imaging for evaluation of VBA plaque were reviewed. The presence and location of VBA plaque and IPH on SNAP were determined. The signal intensity (SI) of the VBA plaque on T1W and TOF imaging was manually measured and the SI ratio against adjacent muscles was calculated. The receiver-operating characteristic (ROC) curve was used to compare the diagnostic accuracy for detecting VBA IPH. Results: Of 87 patients, 67 had IPH and 20 had no IPH on SNAP. The SI ratio between VBA IPH and temporalis muscle on T1W was significantly higher than that in the no-IPH group (235.9 ± 16.8 vs. 120.0 ± 5.1, P < 0.001). The SI ratio between IPH and temporalis muscle on TOF was also significantly higher than that in the no-IPH group (236.8 ± 13.3 vs. 112.8 ± 7.4, P < 0.001). Diagnostic efficacies of SI ratios on TOF and TIW were excellent (AUC: 0.976 on TOF and 0.964 on T1W; cutoff value: 136.7% for TOF imaging and 135.1% for T1W imaging). Conclusion: Compared with SNAP, cutoff levels of the SI ratio between VBA plaque and temporalis muscle on T1W and TOF imaging for detecting IPH were approximately 1.35 times.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Journal of Veterinary Clinics
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• v.40 no.1
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• pp.31-37
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• 2023

Vertebral left atrial size (VLAS) is an important indicator to predict myxomatous mitral valve degeneration (MMVD) in dogs. When the caudal margin of cardiac silhouette and the dorsal margin of caudal vena cava (CdVC) could not be seen exactly, another way to evaluate VLAS is needed. The objective of this study was to assess whether a new modified VLAS (m-VLAS) could be used as an indicator to predict MMVD in 57 small breed dogs with MMVD. The m-VLAS was also used to classify American College of Veterinary Internal Medicine staging groups and left heart enlargement confirmed with echocardiograph (EchoLHE) groups. The m-VLAS was measured as the distance from the ventral aspect of the carina to the dorsal aspect of the intersection of the cardiac silhouette and the farthest LA caudal margin, not the CdVC, followed by drawing the same line beginning at the cranial edge of T4. Based on VLAS values and m-VLAS values measured for dogs with MMVD, correlations between these values and left heart enlargement groups were then evaluated. There were significant differences in both the VLAS and the m-VLAS between EchoLHE groups. The AUC of the ROC curve of the m-VLAS to detect EchoLHE was higher than that of the VLAS. The optimal cutoff value for the m-VLAS was >2.7, which had a higher specificity (86.84%) than the VLAS specificity (71.05%). This study reveals that a new m-VLAS is a more specific indicator than the VLAS for predicting left side heart enlargement in small breed dogs. Therefore, the m-VLAS can be used as a clinically useful radiographic measurement alternative to or better than the VLAS.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Journal of Korean Critical Care Nursing
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• v.16 no.3
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• pp.24-33
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• 2023

Purpose : This study aims to establish an optimal cut-off score on the Braden scale for the assessment of pressure injury to detect pressure injury risks among inpatients in a South Korean tertiary hospital. Methods : This retrospective study used electronic medical records, from January to December 2022. A total of 654 patients were included in the study. Of these, 218 inpatients with pressure injuries and 436 without pressure injuries were classified and analyzed using 1:2 Propensity Score Matching (PSM), and the generalized estimating equation was performed using SPSS Version 26 and the R Machlt package program. Results : The cut-off value on the Braden scale for distinguishing pressure injury was 17 points, and the AUC (area under the ROC curve) was 0.531 (0.484-0.579). The sensitivity was 56.6% (45.5-67.7%) and the specificity was 69.7% (66.0-73.4%). With 17 points, the Braden scale cut-off distinguished those who had pressure injuries from those who did not at the time of admission (p < .03). In the pressure injury group, the Braden score on the day of the pressure injury was 14, with significant results in all subcategories except the moisture category. Conclusion : Our findings revealed that a cut-off value of 17 was optimal for predicting the risk of pressure injuries among tertiary hospital inpatients. Future studies should evaluate the optimal cut-off values in different clinical environments. Additionally, it is necessary to conduct multicenter large sample studies to verify the effectiveness of a 17 value in PI risk assessments.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.39-46
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• 2024

Many researchers make efforts to evaluate water quality using various models. Such models require a dataset without missing values, but in real world, most datasets include missing values for various reasons. Simple deletion of samples having missing value(s) could distort distribution of the underlying data and pose a significant risk of biasing the model's inference when the missing mechanism is not MCAR. In this study, to explore the most appropriate technique for handing missing values in water quality data, several imputation techniques were experimented based on existing KNN and MICE imputation with/without the generative neural network model, Autoencoder(AE) and Denoising Autoencoder(DAE). The results shows that KNN and MICE combined imputation without generative networks provides the closest estimated values to the true values. When evaluating binary classification models based on support vector machine and ensemble algorithms after applying the combined imputation technique to the observed water quality dataset with missing values, it shows better performance in terms of Accuracy, F1 score, RoC-AuC score and MCC compared to those evaluated after deleting samples having missing values.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Laboratory Medicine Online
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• v.6 no.1
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• pp.25-30
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• 2016

Background: The cell cycle-dependent enzyme thymidine kinase 1 (TK1) is known to increase during cancer cell proliferation and has been reported as a prognostic marker for various hematologic malignancies and solid tumors. This study aimed to determine the reference interval in Korean healthy controls and to evaluate the usefulness of TK1 as a biomarker for aggressive clinical behavior in B-cell lymphoma patients. Methods: We enrolled 72 previously untreated patients with B-cell lymphoma and 143 healthy controls. Serum TK1 levels were measured by chemiluminescence immunoassay ($Liaison^{(R)}$, DiaSorin, USA). We established the reference intervals in healthy controls. The diagnostic performance of serum TK1 was studied using receiver operating characteristic (ROC) analysis, and the correlation between the cutoff level for serum TK1 and clinical characteristics of B-cell lymphoma was evaluated. Results: The reference range (95th percentile) of serum TK1 in healthy controls was 5.4-21.8 U/L. There was a clear difference in TK1 levels between patients with B-cell lymphoma and healthy controls ($40.6{\pm}68.5$ vs. $11.8{\pm}4.4U/L$, P <0.001). The area under the curve of serum TK1 for the diagnosis of B-cell lymphoma was 0.73 (cutoff, 15.2 U/L; sensitivity, 59.7%; specificity, 83.2%). An increased TK1 level (${\geq}15.2U/L$) correlated with the advanced clinical stage (P <0.001), bone marrow involvement (P =0.013), international prognostic index score (P =0.001), lactate dehydrogenase level (P =0.001), low Hb level (<12 g/dL) (P =0.028), and lymphocyte count (P =0.023). Conclusions: The serum TK1 level could serve as a useful biomarker for aggressive clinical behavior in B-cell lymphoma patients.