• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1445-1448
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• 2016

Purpose: The current study aimed at assessing the association between neutrophil-lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR) for the prognosis of the surgical outcome of epithelial ovarian cancer (EOC). Materials and Methods: EOC patient medical records of surgical operations between January, 2005 and December, 2015 were reviewed and their data of clinicopathological complete blood counts (CBCs) and surgical outcomes were collected. To assess their effects on surgical outcomes, PLR and NLR optimal predictive values were determined and then compared with each other. Results: A statistically significant relation was found between surgical outcomes and NLR and PLR (p<0.001 and p<0.001), for which new cutoff points were gained (PLR: 192,3,293; NLR: 3). The sensitivity and specificity were 0.74 and 0.67, respectively for PLR and 0.74 and 0.58, for NLR. Conclusions: NLR and PLR seem to be useful methods for the prediction of surgical outcomes in patients with EOCs. Increased NLR and PLR proved to be beneficial for poor surgical outcomes. Moreover, PLR increase showed further help in the predicting outcome of EOC suboptimal debulking.

• Journal of Korean Critical Care Nursing
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• v.8 no.1
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• pp.71-79
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• 2015

Purpose: The purpose of this study was to compare the applicability of the Charlson Comorbidity Index (CCI) and Acute Physiology, Age, Chronic Health Evaluation III (APACHE III) to the prediction of the healthcare outcomes of intensive care unit (ICU) patients. Methods: This research was performed with 136 adult patients (age>18 years) who were admitted to the ICU between May and June 2012. Data were measured using the CCI score with a comorbidity index of 19 and the APACHE III score on the standard of the worst result with vital signs and laboratory results. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under an ROC curve (AUC). Calibration was performed using logistic regression. Results: The overall mortality was 25.7%. The mean CCI and APACHE III scores for survivors were found to be significantly lower than those of non-survivors. The AUC was 0.835 for the APACHE III score and remained high, at 0.688, for the CCI score. The rate of concordance according to the CCI and the APACHE III score was 69.1%. Conclusion: The route of admission, days in ICU, CCI, and APACHE III score are associated with an increased mortality risk in ICU patients.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.624-630
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• 2012

The peer review learning is a method which improves learning outcome of students through feedback between students and the observation and analysis of other students. One of the important problems in a peer review system is to find proper evaluators to each learner considering characteristics of students for improving learning outcomes. Some of peer review systems randomly assign peer review evaluators to learners, or chose evaluators based on limited strategies. However, these systems have a problem that they do not consider various characteristics of learners and evaluators who participate in peer reviews. In this paper, we propose a novel prediction approach of learning outcomes to apply peer review systems considering various characteristics of learners and evaluators. The proposed approach extracts representative attributes from the profiles of students and predicts learning outcomes using various regression models. In order to verify how much outliers affect on the prediction of learning outcomes, we also apply several outlier removal methods to the regression models and compare the predictive performance of learning outcomes. The experiment result says that the SVR model which does not removes outliers shows an error rate of 0.47% on average and has the best predictive performance.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.125-135
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• 2000

Many different statistical and artificial intelligent techniques have been applied to improve the predictability of credit rating. Hybrid models and systems have also been developed by effectively combining different modeling processes or combining the outcomes of individual models. In this paper, we introduced the rough set theory and developed a hybrid credit rating system that combines individual outcomes in terms of rough set theory. An experiment was conducted to compare the prediction capability of the system with those of other methods. The proposed system based on rough set method outperformed the others.

• Journal of Obesity & Metabolic Syndrome
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• v.27 no.4
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• pp.213-222
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• 2018

Bariatric surgery has evolved from a surgical measure for treating morbid obesity to an epochal remedy for treating metabolic syndrome as a whole, which is represented by type 2 diabetes mellitus. Numerous clinical trials have advocated bariatric or metabolic surgery over nonsurgical interventions because of markedly superior metabolic outcomes in morbidly obese patients who satisfy traditional criteria for bariatric surgery (body mass index [BMI] >$35kg/m^2$) and in less obese or simply overweight patients. Nevertheless, not all diabetes patients achieve the most desirable outcomes; i.e., diabetes remission after metabolic surgery. Thus, candidates for metabolic surgery should be carefully selected based on comprehensive preoperative assessments of the risk-benefit ratio. Predictors for diabetes remission after metabolic surgery may be classified into two groups based on mechanism of action. The first is indices for preserved pancreatic beta-cell function, including younger age, shorter duration of diabetes, and higher C-peptide level. The second is the potential for an insulin resistance reduction, including higher baseline BMI and visceral fat area. Several prediction models for diabetes remission have been suggested by merging these two to guide the joint decision-making process between clinicians and patients. Three such models, DiaRem, ABCD, and individualized metabolic surgery scores, provide an intuitive scoring system and have been validated in an independent external cohort and can be utilized in routine clinical practice. These prediction models need further validation in various ethnicities to ensure universal applicability.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• Knowledge Management Research
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• v.14 no.2
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• pp.49-69
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• 2013

In this study, we examined how the psychological safety influence expected image outcomes(image gain, image risk) and how expected image outcomes impact one's involvement in creative work. And, we examined the relationship between creative work involvement and job performance. We also investigated mediating role of expected image outcomes between psychological safety and creative work involvement. Survey data was collected from 271 employees working in a South Korea. The results of structural equation modeling using LISREL 8.50 indicate that psychological safety was positively related to expected image gain, but not significantly related to expected image risk. The result also expected image gain was positively related to creative work involvement, contrary to prediction of hypothesis 2-2, expected image risk was positively associated with creative work involvement. In addition, creative work involvement has positive effect on job performance. Finally, the relationship of expected image outcomes about psychological safety and creative work involvement was mediated by expected image gain partially. We extended the literature and contributed better understanding of the creativity research by answering calls for further research on involvement in creative work rather then the outcome of the creative process.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• Archives of Plastic Surgery
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• v.41 no.2
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• pp.174-180
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• 2014

It is well known that patients with severe facial injuries accompanied by facial disfiguration are vulnerable to a poor quality of life. With the help of facial transplantation, it has become possible to achieve an optimal anatomical reconstruction. As compared to conventional methods, it can provide more desirable functional, aesthetic, and psychosocial outcomes. Face transplantation surgeons need to consider many problems associated with the criteria for selecting patients, techniques for harvesting the donor tissue, prediction of the expected functional outcomes, limitations in obtaining written informed consent for conducting the procedure, evaluation of the post-transplant immunological response and postoperative immunosuppressant requirements, psychological and social outcomes for the patients, and other concerns about funding and ethical issues.