• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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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.

• 중환자간호학회지
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• 제8권1호
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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.

• 한국지능시스템학회논문지
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• 제22권5호
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• pp.624-630
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• 2012

피어리뷰(peer review)를 통한 학습은 학습자간 피드백을 주고받으며 다양한 정보를 관찰, 분석하는 과정을 통해 학습성과를 향상시키는 방법이다. 피어리뷰 시스템의 중요한 문제 중 하나는, 학습자의 여러 특징을 고려하여 학습자의 학습성과를 향상시키는데 적합한 평가자를 찾는 것이다. 그러나 기존 피어리뷰 시스템에서는 학습자들이 가지는 다양한 특징을 고려하지 않고 단순히 피어리뷰 평가자를 임의로 할당하거나 제한적인 학습 전략에 따라 피어리뷰 평가자를 편성하였다. 본 논문에서는 학습자와 평가자의 다양한 특징을 고려하여, 특정 학습자와 평가자의 조합으로 피어리뷰 학습이 이루어졌을 때 학습자에게 어느 정도의 학습성과 향상이 있을지 예측하는 방법을 제안한다. 제안하는 방법은 학습자와 평가자의 프로파일 정보로부터 대표 속성을 추출하고 다양한 회귀 모델을 적용하였다. 또한 학습자들의 다양한 특징으로 인하여 나타날 수 있는 이상치(outlier)가 학습성과 예측에 미치는 영향을 알아보기 위해, 회귀 모델에 다양한 이상치 제거 방법을 적용하여 학습성과 예측성능을 비교하였다. 실험 결과 이상치를 제거 하지 않은 SVR 모델이 평균 0.47%의 에러율을 보이며 가장 우수한 학습성과 예측결과를 보였다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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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 Obesity & Metabolic Syndrome
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• 제27권4호
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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.

• 한국경영과학회지
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• 제25권3호
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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 Korean Neurosurgical Society
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• 제67권1호
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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.

• 지식경영연구
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• 제14권2호
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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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• 제26권3호
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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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• 제41권2호
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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.