• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.11
• /
• pp.4913-4916
• /
• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Korean Journal of Veterinary Research
• /
• v.53 no.2
• /
• pp.77-82
• /
• 2013

The present study was planned to evaluate the bioequivalence of two commercial formulations of enrofloxacin, which have been marketed as 10% injectable solution after intramuscular administration at a single dose of 2.5 mg/kg body weight to 12 clinically healthy goats The study was carried out on the basis of crossover design. The two formulations were: Baytril as a reference product and Spectrama Vet as a test product. The plasma concentrations of enrofloxacin were measured by high performance liquid chromatography (HPLC) with UV detector. The pharmacokinetics of that data was performed using non-compartmental analysis. The maximum plasma concentration ($C_{max}$), time to reach peak concentration ($T_{max}$), area under concentration-time curve (AUC), elimination half-life ($t_{0.5el}$) were 1.14 and $1.05{\mu}g/mL$, 0.79 and 0.83 h, 5.70 and $5.79{\mu}g.h/mL$, 5.19 and 5.39 h for Baytril and Spectrama Vet, respectively. The 90% confidence interval for the mean ratio of $T_{max}$, $C_{max}$ and AUC were 94.72-116.2, 87.88-97.16 and 86.44-118.72%, respectively. These values falls within the European Medicines Agency bioequivalence acceptance range of 80-125% for both $T_{max}$ and AUC and between 75-133% for $C_{max}$. In conclusion, Spectrama-Vet is bioequivalent to Baytril and both products can be used as interchangeable drug in veterinary medicine practice.

• Biomolecules & Therapeutics
• /
• v.6 no.4
• /
• pp.400-405
• /
• 1998

The pharmacokinetics of CJ-50001 (recombinant human granulocyte-colony stimulating factor, developed by R&D center of Cheil Jedang Corp.) were investigated in rats and dogs. The serum concentrations of CJ-50001 were measured by a sandwich enzyme immunoassay. After single intravenous (iv) administration of Cf-50001 to rats at a dose of 5 $\mu$g/kg, the mean terminal half-life and area under the concentration-time curve (AUC) were 0.96 h and 124.497g . h/ml, respectively. After single subcutaneous (sc) administration at the same dose, maximum serum concentration was observed at about 2 hours after administration, and the mean terminal half-life, AUC and the bioavailability were 1.11 h,63.58$\mu$g . h/ml and 51.07%, respectively. In repeated dosing studies, CJ-50001 was administered iv and sc to rats at a daily dose of 5$\mu$g/kg for 7 days. The pharmacokinetic parameters, such as mean AUC and terminal half-life, were no significantly different from those of single administration. Following single iv and sc administration of CJ-50001 to dogs at a dose of 5 $\mu$g/kg, mean AUCs were much higher than those of rats, due to the decreased clearence (CL). After sc administration to dogs, maximum serum concentration was observed at 2~4 hours after administration and the bioavailability was 54.60%.

• Archives of Pharmacal Research
• /
• v.17 no.2
• /
• pp.80-86
• /
• 1994

The bioavailability of digoxin generic tablets manufactures in Korea (formulations A & B) wwere compared to a standard (formulation C; Lanoxin brand digoxin, Burroughs Wellcome, USA) in 12 healthy Korean male volunteers (mean age 31.4 years) in a single dose, randomized, complete block crossover study. Using a latin square design, each of the subjects was randomized to the order number and allocated to each of the three treatments of 0.5mg oral digoxin. Digoxin conc4ntrations in serum and urine samples collected for 48 hours after dosing were measured by fluoprescence polarization immunoassy and radioimmunoassy, respectively. Treatments were compared by using nonlinear least squares regession analysis to evaluate the following pharmacokinetic parameters : maximum serum concentation $(C_{max})$; time of maximum serum concentation $(T_{max})$; area under the serum concentration-time curve $AUC_{0-12}$, $C_{max}$\;and\;(AUC_{0-12})$; and cummulative urinary excretion for 0-48 hours $(CLE_{0-48}.\;Mean\;AUC_{0-12},\;C_{max},\;and\;CUE_{0-48}$ values for formulations B and C were significantly different from formulation A (P<0.001), but not significantly diffeerent form each other. Basede on $AUL_{0-12}\;and\;CUE_{0-48}$ respectively, the relative availability of formulation B was 87.5% and 89.6% and the relative availability of formultation A was 43% and 35% when compared to formulation C(the standard).

• Korean Journal of Clinical Pharmacy
• /
• v.9 no.2
• /
• pp.92-96
• /
• 1999

The pharmacokinetics of carbamazepine(100 mg/kg, oral) in the folic acid-induced renal failure rabbits was studied. Renal failure was induced by the i.v. injection of folic acid (50, 100, and 200 mg/kg). At folic acid dose of 100 and 200 mg/kg, the serum creatinine concentration (Scr) and blood urea nitrogen (BUN) increased significantly compared with control rabbits. Plasma concentrations and area under the plasma level-time curve (AUC) of carbamazepine increased significantly at folic acid dose of 100 and 200 mg/kg. The elimination rate constant (Kel) of carbamazepine decreased significantly, and half-life $(t_{1/2})$ of carbamazepine increased significantly at folic acid dose of 100 and 200 mg/kg. The serum creatinine concentration (Scr) correlated well with AUC and elimination rate constant (Kel) of carbamazepine, as well as BUN with AUC and elimination rate constant (Kel) of carbamazepine. These results suggest that adjustment of the dosage regimen of carbamazepine is desirable, and serum creatinine concentration (Scr) as well as BUN can be used for adjusting the dosage regimen of carbamazepine in renal failure.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.435-447
• /
• 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
• /
• v.81 no.1
• /
• pp.157-165
• /
• 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
• /
• v.8 no.11
• /
• pp.433-440
• /
• 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
• /
• v.15 no.5
• /
• pp.345-349
• /
• 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
• /
• v.27 no.2
• /
• pp.65-80
• /
• 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.