• Journal of Gastric Cancer
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• v.19 no.2
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• pp.183-192
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• 2019

Purpose: Due to adverse events, dose reduction or withdrawal of adjuvant chemotherapy is required for some patients. To identify the predictive factors for tolerability to postoperative adjuvant S-1 monotherapy in gastric cancer (GC) patients, we evaluated the predictive values of blood indicators. Materials and Methods: We analyzed 98 patients with pStage II/III GC who underwent postoperative adjuvant S-1 monotherapy. We retrospectively analyzed correlations between 14 parameters obtained from perioperative routine blood tests to assess their influence on the withdrawal of postoperative adjuvant S-1 monotherapy, within 6 months after discontinuation. Results: Postoperative adjuvant chemotherapy was discontinued in 21 patients (21.4%) within 6 months. Univariable analysis revealed that high preoperative albumin-bilirubin (ALBI) scores had the highest odds ratio (OR) for predicting the failure of adjuvant S-1 chemotherapy (OR, 6.47; 95% confidence interval [CI], 2.08-20.1; cutoff value, -2.696). The high ALBI group had a significantly shorter time to failure of postoperative adjuvant S-1monotherapy (hazard ratio, 3.48; 95% CI, 1.69-7.25; P=0.001). Multivariable analysis identified high preoperative ALBI score as an independent prognostic factor for tolerability (OR, 10.3; 95% CI, 2.33-45.8; P=0.002). Conclusions: Preoperative ALBI shows promise as an indicator associated with the tolerability of adjuvant S-1 monotherapy in patients with pStage II/III GC.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• Quality Improvement in Health Care
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• v.7 no.2
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• pp.218-228
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• 2000

Background : Among 'structure', 'process' and 'outcome' approaches, outcome evaluation is considered as the most direct and best approach to assess the quality of health care providers. Risk-adjustment is an essential method to compare outcome across providers. This study has aims to judge performance of hospitals by severity adjusted mortality rates of coronary artery bypass graft (CABG) surgery. Methods : Medical records of 584 patients who got the CABG surgery in 6 general hospitals during 1996 and 1997 were reviewed by trained nurses. The MedisGroups was used to quantify severity of patients. The predictive probability of death was calculated for each patient in the sample from a multivariate logistic regression model including the severity score, age and sex. For evaluation of hospital performance, we calculated ratio of observed number to expected number of deaths and z score [(observed number of deaths - expected number of deaths)/square root of the variance in the number of deaths], and compared observed mortality rate with confidence interval of adjusted mortality rate for each hospital. Results : The overall in-hospital mortality was 7.0%, ranged from 2.7% to 15.7% by hospital. After severity adjustment the mortality by hospital was from 2.7% to 10.7%. One hospital with poor performance was distinctly divided from others with good performance. Conclusion : In conclusion, severity-adjusted mortality rate of CABG surgery might be applied as an indicator for hospital performance evaluation in Korea. But more pilot studies and improvement of methodologies has to be done to use it as quality indicator.

• Journal of Periodontal and Implant Science
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• v.47 no.4
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• pp.240-250
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• 2017

Purpose: The aim of this study was to analyze the prevalence and risk indicators of peri-implantitis in Korean patients with history of periodontal disease. Methods: A total of 444 patients with 1,485 implants were selected from patients who had been treated at the Department of Periodontology, Chonbuk National University Dental Hospital between July 2014 and June 2015. A group with a history of peri-implantitis (HP) (370 patients with 1,189 implants) and a group with a current peri-implantitis (CP) (318 patients with 1,004 implants) were created based on the radiographic and clinical assessments of implants. The prevalence of peri-implantitis was calculated at both the patient and implant levels. The influence of risk variables on the occurrence of peri-implantitis was analyzed using generalized estimating equations analysis. Results: The prevalence of peri-implantitis in the HP and CP groups ranged from 6.7% to 19.7%. The cumulative peri-implantitis rate in the HP group estimated with the Kaplan-Meier method was higher than that in the CP group over the follow-up period. Among the patient-related risk variables, supportive periodontal therapy (SPT) was the only significant risk indicator for the occurrence of peri-implantitis in both groups. In the analysis of implant-related variables, implants supporting fixed dental prosthesis (FDP) and implants with subjective discomfort was associated with a higher prevalence of peri-implantitis than single implants and implants without subjective discomfort in the HP group. The presence of subjective discomfort was the only significant implant-related variable predictive of peri-implantitis in the CP group. Conclusions: Within the limitations of this study, the prevalence of peri-implantitis in Korean patients with a history of periodontal disease was similar to that reported in other population samples. Regular SPT was important for preventing peri-implantitis. Single implants were found to be less susceptible to peri-implantitis than those supporting FDP. Patients' subjective discomfort was found to be a strong risk indicator for peri-implantitis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.71-84
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• 2017

Radon, which enters the interior environment from soil, rocks, and groundwater, is a radioactive gas that poses a serious risk to humans. Indoor radon concentrations are measured to investigate the risk of radon gas exposure and reliable radon concentration mapping is then performed for further analysis. In this study, we compared the predictive performance of various univariate kriging algorithms, including ordinary kriging and three nonlinear transform-based kriging algorithms (log-normal, multi-Gaussian, and indicator kriging), for mapping radon concentrations with an asymmetric distribution. To compare and analyze the predictive performance, we carried out jackknife-based validation and analyzed the errors according to the differences in the data intervals and sampling densities. From a case study in South Korea, the overall nonlinear transform-based kriging algorithms showed better predictive performance than ordinary kriging. Among the nonlinear transform-based kriging algorithms, log-normal kriging had the best performance, followed by multi-Gaussian kriging. Ordinary kriging was the best for predicting high values within the spatial pattern. The results from this study are expected to be useful in the selection of kriging algorithms for the spatial prediction of data with an asymmetric distribution.

• 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 Biomedical Engineering Research
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• v.43 no.1
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• pp.19-26
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• 2022

The Comprehensive in vitro Proarrhythmic Assay(CiPA) project was launched for solving the hERG assay problem of being classified as high-risk groups even though they are low-risk drugs due to their high sensitivity. CiPA presented a protocol to predict drug toxicity using physiological data calculated based on the in-silico model. in this study, features calculated through the in-silico model are analyzed for correlation of changing action potential in the near future, and features are verified through predictive performance according to drug datasets. Using the O'Hara Rudy model modified by Dutta et al., Pearson correlation analysis was performed between 13 features(dVm/dtmax, APpeak, APresting, APD90, APD50, APDtri, Ca_peak, Ca_resting, CaD90, CaD50, CaDtri, qNet, qInward) calculated at 100 pacing, and between dVm/d_{tmax_repol} calculated at 1,000 pacing, and linear regression analysis was performed on each of the 12 training drugs, 16 verification drugs, and 28 drugs. Indicators showing high coefficient of determination(R²) in the training drug dataset were qNet 0.93, AP resting 0.83, APDtri 0.78, Ca resting 0.76, dVm/dt_max 0.63, and APD90 0.61. The indicators showing high determinants in the validated drug dataset were APDtri 0.94, APD90 0.92, APD50 0.85, CaD50 0.84, qNet 0.76, and CaD90 0.64. Indicators with high coefficients of determination for all 28 drugs are qNet 0.78, APD90 0.74, and qInward 0.59. The indicators vary in predictive performance depending on the drug dataset, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug dataset, and the entire drug dataset.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.3
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• pp.282-290
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• 2013

A primary air pollutant as an indicator of air quality released from incomplete combustion is Carbon monoxide. A study of the distributions of CO concentration with no heat source in a tunnel model closed at left end side is simulated with a commercial CFD code. The tunnel model is used to investigate the CO concentration distributions at three Reynolds numbers of 990, 1970, and 3290. which are computed by the inlet velocities of 0.3, 0.6 and 1.0 m/s. The CFD predictive approaches can be useful for a better design to analyze the distributions of CO concentrations. In the case of the tunnel model closed at left end side alone, the concentration changes of x/H=-5 and -2.5 have the similar laminar characteristics like the case of the tunnel model closed at both end sides expecially at low values of Reynolds number. Irregular average CO concentration variations at Re=1790 are considered that the transition from laminar to turbulent flow occurs even in three different tunnel models.