• Journal of Chest Surgery
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• v.54 no.2
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• pp.88-98
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• 2021

Background: This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database. Methods: We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities. Results: Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%. Conclusion: This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

• Journal of Environmental Health Sciences
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• v.45 no.1
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• pp.82-96
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• 2019

Objective: The aims of this study were to provide the basic data for establishing a precautionary management policy and to develop a methodology for selecting a radon management priority target area suitable for the Korean domestic environment. Methods: A suitable mapping method for the domestic environment was derived by conducting a quantitative comparison of predicted values and measured values that were calculated through implementation of two models such as IDW and RBF methods. And a qualitative comparison including the clarity of information transmission of the written radon map was carried out. Results: The predicted and measured values were obtained through the implementation of the spatial analysis models. The IDW method showed the lowest in the calculated mean square error and had a higher correlation coefficient than the other methods. As results of comparing the uncertainty using the jackknife concept and the concept of error distance for comparison of the differences according to the model interpolation method, the sum of the error distances showed a modest increase compared with the RBF method. As a result of qualitatively comparing the information transfer clarity between the radon maps prepared with the predicted values through the model implementation, it was found that the maps plotted using the predicted values by the implementation of the IDW method had greater clarity in terms of highness and lowness of radon concentration per area compared with the maps plotted by other methods. Conclusions: The radon management priority area suggests selecting a metropolitan city including an area with a high radon concentration.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.434-444
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• 2013

In order to evaluate drought risk at upland according to climate change scenario (RCP8.5), we have carried out the simulation using agricultural water balance estimation model, called AFKAE0.5, at 66 weather station sites in 2020, 2046, 2050, 2084, and 2090. Total Drought Risk Index between the first month (f) and last month (l) (TDRI(f/l)) and maximum continuous drought risk index (MCDRI(f/l)) were defined as the index for analyzing pattern and strength of drought simulated by the model. Based on distribution maps of MCDRI (1/12), drought strength was predicted to be most severe in 2084 for all regions. Some regions showed severe risk of drought meaning over 20 days of MCDRI (1/12) in the other years, while MCDRI (1/12) in other regions did not reach 5 days. Even though maximum value of TDRI (1/12) in 2090 was greater than in 2050, more severe drought risk in 2050 than in 2090 was predicted based on MCDRI (4/6). It implies that drought risk should be assessed for each crop with its own growing season.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.295-303
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• 2019

A heart simulator, UT-Heart, is a finite element model of the human heart that can reproduce all the fundamental activities of the working heart, including propagation of excitation, contraction, and relaxation and generation of blood pressure and blood flow, based on the molecular aspects of the cardiac electrophysiology and excitation-contraction coupling. In this paper, we present a brief review of the practical use of UT-Heart. As an example, we focus on its application for predicting the effect of cardiac resynchronization therapy (CRT) and evaluating the proarrhythmic risk of drugs. Patient-specific, multiscale heart simulation successfully predicted the response to CRT by reproducing the complex pathophysiology of the heart. A proarrhythmic risk assessment system combining in vitro channel assays and in silico simulation of cardiac electrophysiology using UT-Heart successfully predicted drug-induced arrhythmogenic risk. The assessment system was found to be reliable and efficient. We also developed a comprehensive hazard map on the various combinations of ion channel inhibitors. This in silico electrocardiogram database (now freely available at http://ut-heart.com/) can facilitate proarrhythmic risk assessment without the need to perform computationally expensive heart simulation. Based on these results, we conclude that the heart simulator, UT-Heart, could be a useful tool in clinical medicine and drug discovery.

• The Journal of Fisheries Business Administration
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• v.39 no.2
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• pp.109-123
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• 2008

This study is aimed to establish an investment decision model for offshore aquaculture project of rock bream in Korea using a certainty equivalent method (CEM) based on the expected utility theory and to investigate its economic viability under risk and uncertainty. In the analysis with CEM, the effects of risk attitude and risk level on investment and risk premium were examined and the impacts of various risk and uncertainty factors on the investment decision were also assessed. In addition, the outcomes were compared to those evaluated by the traditional net present value (NPV) method. Results show that risk premium grew as the investors became more risk averse and uncertainty level (the variance of NPV) increased. Consequently, the certainty equivalent value was predicted to decrease from the value assessed by the traditional NPV method.

• Journal of Drive and Control
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• v.13 no.4
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• pp.14-22
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• 2016

This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object's dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator's working area is derived based on a kinematic analysis of the excavator's working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator's working parts, an object's behavior and the excavator's working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.

• Journal of Environmental Health Sciences
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• v.44 no.2
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• pp.196-203
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• 2018

Objectives: The purpose of this study was to assess environmental risk on the emerging contaminants of concern, such as ivermetin, parziquantel, tamiflu and triclosan. Furthermore, we tried to provide a more efficient management practice and a basis for future studies of risk assessment on those substances. Methods: Predicted no effect concentration (PNEC) and predicted environmental concentration (PEC) were determined through modeling and literature reviews. Environmental risk assessment was evaluated by calculating HQ (hazard quotient) by a comparison of PEC (or measured environmental concentration (MEC)) and PNEC. Results: HQ value of tamiflu calculated from MEC was 1.9E-03. For ivermectin and triclosan, the HQ values were not available because these were not detected in the aquatic environment. The toxicity of ivermectin and triclosan showed a very low value, indicating a high level of HQ. However, praziquantel can be categorized into the material that do not require management since they have less than HQ 1. Conclusion: Based on the results of the initial risk assessment, it is assumed that the ivermectin and triclosan have potential to cause direct adverse effects on the aquatic environment. To conduct an accurate environmental risk assessment, the further study on PEC estimation of such contaminants should be actively carried out.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5037-5041
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• 2015

Background: An hCG regression curve has been used to predict the natural history and response to chemotherapy in gestational trophoblastic disease. We constructed hCG regression curves in high-risk gestational trophoblastic neoplasia (GTN) treated with EMA/CO and identified an optimal hCG level to detect EMA/CO resistance in GTN. Materials and Methods: Eighty-one women with GTN treated with EMA/CO were classified as primary high-risk GTN (n = 65) and single agent-resistance GTN (n = 16). The hCG levels prior to each course of chemotherapy were plotted in the 10th, 50th, and 90th percentiles to construct the hCG regression curves. Diagnostic performance was evaluated for an optimal cut-off value. Results: The median hCG levels were 264,482 mIU/mL mIU/mL and 495.5 mIU/mL mIU/mL for primary high-risk GTN and single agent-resistance GTN, respectively. The 50th percentile of the hCG level in primary high-risk GTN and single agent-resistance turned to normal before the 4th and the 2nd course of chemotherapy, respectively. The 90th percentile of the hCG level in primary high-risk GTN and single agent-resistance turned to normal before the 9th and the 2nd course of chemotherapy, respectively. The hCG level of ${\geq}118.6mIU/mL$ mIU/mL at the 5thcourse of EMA/CO predicted the EMA/CO resistance in primary high-risk GTN patients with a sensitivity of 85.7% and a specificity of 100%. Conclusion: EMA/CO resistance in primary high-risk GTN can be predicted by using an hCG regression curve in combination with the cut-off value of 118.6 mIU/mL at the 5thcourse of chemotherapy.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.323-334
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• 2015

he current VaR model based on the J.P. Morgan's RiskMetrics structurally can not reflect the future economic situation. In this study, we propose a One-factor model resulting from the Wiener stochastic process decomposed into a systematic risk factor and an idiosyncratic risk factor. Therefore, we are able to perform a preemptive risk management by means of reflecting the predicted common risk factors in the model. Stocks in the portfolio are satisfied with the independence to each other because the common factors are fixed by the predicted value. Therefore, we can easily determine the investment in each stock to minimize the variance of the portfolio. In addition, the portfolio VaR is decomposed into the sum of the individual VaR. So we can effectively implement the constitution of the portfolio to meet the target maximum losses.

• Environmental Engineering Research
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• v.14 no.3
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• pp.186-194
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• 2009

Understanding the environmental fate of human and animal pharmaceuticals and their risk assessment are of great importance due to their growing environmental concerns. Although there are many potential pathways for them to reach the environment, effluents from sewage treatment plants (STPs) are recognized as major point sources. In this study, the removal efficiencies of the 43 selected priority pharmaceuticals in a conventional STP were evaluated using two simple models: an equilibrium partitioning model (EPM) and STPWIN$^{TM}$ program developed by US EPA. It was expected that many pharmaceuticals are not likely to be removed by conventional activated sludge processes because of their relatively low sorption potential to suspended sludge and low biodegradability. Only a few pharmaceuticals were predicted to be easily removed by sorption or biodegradation, and hence a conventional STP may not protect the environment from the release of unwanted pharmaceuticals. However, the prediction made in this study strongly relies on sorption coefficient to suspended sludge and biodegradation half-lives, which may vary significantly depending on models. Removal efficiencies predicted using the EPM were typically higher than those predicted by STPWIN for many hydrophilic pharmaceuticals due to the difference in prediction method for sorption coefficients. Comparison with experimental organic carbon-water partition coefficients ($K_{ocs}) revealed that log KOW-based estimation used in STPWIN is likely to underestimate sorption coefficients, thus resulting low removal efficiency by sorption. Predicted values by the EPM were consistent with limited experimental data although this model does not include biodegradation processes, implying that this simple model can be very useful with reliable Koc values. Because there are not many experimental data available for priority pharmaceuticals to evaluate the model performance, it should be important to obtain reliable experimental data including sorption coefficients and biodegradation rate constants for the prediction of the fate of the selected pharmaceuticals.