• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.435-447
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• 2019

In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.

• Journal of the Korean Society of Radiology
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• v.81 no.1
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• pp.157-165
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• 2020

Purpose To evaluate the optimal measurement location, cut-off value, and diagnostic performance of S-Shearwave in differential diagnosis of breast masses seen on ultrasonography (US). Materials and Methods During the study period, 225 breast masses in 197 women were included. S-Shearwave measurements were made by applying a square region-of-interest automatically generated by the US machine. Shearwave elasticity was measured three times at four different locations of the mass, and the highest shearwave elasticity was used for calculating the optimal cut-off value. Diagnostic performance was evaluated by using the area under the receiving operator characteristic curve (AUC). Results Of the 225 breast masses, 156 (69.3%) were benign and 69 (30.7%) were malignant. Mean S-Shearwave values were significantly higher for malignant masses (108.0 ± 70.0 kPa vs. 43.4 ± 38.3 kPa; p < 0.001). No significant differences were seen among AUC values at different measurement locations. With a cut-off value of 41.9 kPa, S-Shearwave showed 85.7% sensitivity, 63.9% specificity, 70.7% accuracy, and positive and negative predictive values of 51.7% and 90.8%, respectively. The AUCs for US and S-Shearwave did not show significant differences (p = 0.179). Conclusion S-Shearwave shows comparable diagnostic performance to that of grayscale US that can be applied for differential diagnosis of breast masses seen on US.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

• 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 Pharmaceutical Investigation
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• v.14 no.3
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• pp.105-121
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• 1984

The bioavailability of rifampicin (brand A, B and C) was studied and the dissolution by foamed plastic rotating method and basket rotating method was also investigated. The results were as follows; 1. In the case of foamed plastic rotating method, it was revealed that dissolution rate of brand C was most rapid, but in the case of basket rotating method the results revealed that brand B was most rapid. Also it was observed that the dissolution rate in artificial gastric juice was more rapid than one in artificial intestinal juice, and that Avicel added in capsule increased additively the dissolution rate, particulary brand B. 2. Relative systemic availability by urine data showed that the results from all capsules filled with brand A, B and C were identical but in the case of the ripamficin capsules filled with Avicel, the results showed that Avicel increased the availability of brand A and B. 3. Area under serum concentration curve $(0{\sim}8hrs)$ was in order of $brand\;A{\fallingdotseq}brand\;C$ > brand B, but Avicel increased significantly the AUC of brand B and showed no effect in others. 4. Relative systemic availability calculated with excreted amount of rifampicin in urine was similar in each rifampicin capsules. In rifampicin (A) and rifampicin (B), Avicel which added in capsules appeared increasing tendency in urine excretion of rifampicin, but in rifampicin (C) it did not appeared. 5. Area under serum concentration curve $(0{\sim}8hrs)$ in rifampicin capsules was in order of $rifampicin(A){\fallingdotseq}rifampicin(C)$>rifampicin(B). In rifampicin (B) with Avicel capsules, area under serum concentration curve (0-8hrs.) increased significantly and in others insignificantly.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.3
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• pp.167-173
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• 2022

Objectives: The purpose of this study was to estimate the minimal clinically important difference (MCID) of mouth opening (MO) and patient satisfaction in surgically treated oral submucous fibrosis (OSMF) patients. Materials and Methods: The status of MO was collected preoperatively (T0), postoperatively at 3 months (T1), and at a minimum of 6 months postoperatively (T2). MCID was determined through the anchor-based approach with the change difference method, mean change method, and receiver operator characteristic curve (ROC) method. Results: In this study, 35 patients enrolled and completed postoperative follow-up (T2) averaging a duration of 18.1 months. At T1, using the change difference method, MO was 14.89 mm and the ROC curve exhibited a 11.5 gain in MO (sensitivity 81.8% and specificity 100%, area under the curve [AUC] of 0.902) and was classified as MCID as reported by patients. At T2, MCID of MO was 9.75 mm using the change difference method and 11.75 mm by the mean change method. The ROC curve revealed that the MCID of MO at T2 was 10.5 mm with 73.9% sensitivity and 83.3% specificity (AUC of 0.873). The kappa value was 0.91, confirming reliability of the data. Conclusion: This study demonstrated MCID values that indicate the clinical relevance of surgical treatment of OSMF if the minimum possible gain in MO is approximately 10 mm.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Bulletin of the Korean Chemical Society
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• v.35 no.2
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• pp.557-561
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• 2014

The aim of the present study was to improve dosing of lacosamide, a functionalized amino acid used as an antiepileptic agent, from twice daily to once daily for the convenience of patients. A sustained-release lacosamide tablet was developed and dissolution testing was employed to determine in vitro release behavior using water or buffer solutions at pH 1.2, 4.0, or 6.8. Lacosamide was released for 12 h from the sustainedrelease (SR) tablet, as compared to complete release within 1 h from an immediate-release $Vimpat^{(R)}$ tablet. Each formulation (100 mg) was orally administered to six beagle dogs and six mini-pigs under fasted conditions, and pharmacokinetic parameters such as the area under the concentration time curve ($AUC_t$), the maximum plasma concentration ($C_{max}$), and the time at which this occurred ($T_{max}$) were calculated. These results showed similar values for $AUC_t$, $C_{max}$, and $T_{max}$ following oral administration of immediate-release ($Vimpat^{(R)}$) and SR lacosamide tablets.

• Journal of Veterinary Clinics
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• v.23 no.4
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• pp.416-420
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• 2006

Compare to testing sera individually, pooled-serum testing has considered as a cost-effective method, particularly on a large population-based seroprevalence studies. This study was to determine the relationship between individual sera and pooled sera titers for detection of avian infectious bronchitis virus (IBV) and to evaluate suitability of pooled sera by comparing prevalences estimated from both samples. A total of 5,000 individual samples were collected from 500 flocks in Chungcheong, Gyunsgi, and Kangwon provinces between January 2005 and February 2006. Ten samples were randomly selected from each flock. Five-hundred pooled sera were prepared by mixing equal amount of each 10 individual serum from the original samples. IBV antibody titers were measured by hemagglutination inhibition (HI) test. The least squares regression analysis was performed to construct equation between pooled and mean individual titers. To determine whether the flock is infected 4 arbitrary criteria were used: detection of at least 1 chicken with HI titer ${\ge}$ 9 (criterion 1), detection of at least 2 samples with HI titer ${\ge}$9 (criterion 2), detection of at least 1 sample with HI titer ${\ge}$ 10 (criterion 3), and filially detection of at least 1 sample with HI titer ${\ge}$ 11 (criterion 4). The receiver operating characteristic (ROC) curve was used to examine the cut-off points of pooled titers showing optimal diagnostic accuracy. The area under the curve (AUC), sensitivities (Se), specificities (Sp), and positive (PPV) and negative (NPV) predictive values were calculated. The regression equation between pooled titers (pool) and mean individual titers (mean) was: $pool= 1.2498+0.8952{\times}mean$, with coefficient of determination of 87% (p< 0.0001). The optimal cut-off points of pooled titers were titer 8 for criterion 1 (AUC=0.975, Se=0.883, Sp=0.959, PPV=0.985, NPV=0.728), titer 8 for criterion 2 (AUC=0.969, Se=0.954, Sp=0.855, PPV=0.926, NPV=0.907), titer 9 for criterion 3 (AUC=0.970, Se=0.836, Sp=0.967, PPV=0.978, NPV=0.772), and titer 9 for criterion 4 (AUC= 0.946, Se=0.928, Sp=0.843, PPV=0.857, NPV=0.921). The difference of 'prevalence estimated by individual and pooled sample showed a minimum of 2% for criteria 2 and a maximum of 9.1:% for criteria 3. These results indicate that the use of pooled sera in HI test for screening IBV infection in laying hen flocks is considered as a cost-effective method of testing large numbers of samples with high diagnostic accuracy.