• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.55-58
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• 2000

To achieve high performance by exploiting instruction level parallelism(ILP) aggressively in superscalar processors, value prediction is used. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively it's data dependent instruction based on the predicted outcome. In this paper, the performance of a hybrid value prediction scheme with dynamic classification mechanism is measured and analyzed by using execution-driven simulator for SPECint95 benchmark set.

• Physical Therapy Korea
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• v.17 no.3
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• pp.59-67
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• 2010

Mortality rates from stroke have been declining. Because of this, more people are living with residual disability. Rehabilitation plays an important role in functional recovery of stroke survivors. In stroke rehabilitation, early prediction of the obtainable level of functional recovery is desirable to deliver efficient care, set realistic goals, and provide appropriate discharge planning. The purpose of this study was to identify predictors of functional outcome after stroke using inpatient rehabilitation as measured by Functional Independence Measure (FIM) total scores. Correlation and stepwise multiple regression analyses were performed on data collected retrospectively from two-hundred thirty-five patients. More than moderate correlation was found between FIM total scores at the time of hospital admission and FIM total scores at the time of discharge from the hospital. Significant predictors of FIM at the time of discharge were FIM total scores at the time of hospital admission, age, and onset-admission interval. The equation was as follows: expected discharge FIM total score = $76.12+.62{\times}$(admission FIM total score)-$.38{\times}(age)-.15{\times}$(onset-admission interval). These findings suggest that FIM total scores at the time of hospital admission, age, and onset-admission interval are important determinants of functional outcome.

• Radiation Oncology Journal
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• v.30 no.2
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• pp.70-77
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• 2012

Purpose: To evaluate the patterns of tumor shape and to compare tumor volume derived from simple diameter-based ellipsoid measurement with that derived from tracing the entire tumor contour using region of interest (ROI)-based 3D volumetry with respect to the prediction outcome in cervical cancer patients treated with concurrent chemotherapy and radiotherapy. Materials and Methods: Magnetic resonance imaging was performed in 98 patients with cervical cancer (stage IB-IIIB). The tumor shape was classified into two categories: ellipsoid and non-ellipsoid shape. ROI-based volumetry was derived from each magnetic resonance slice on the work station. For the diameter-based surrogate "ellipsoid volume," the three orthogonal diameters were measured to calculate volume as an ellipsoid. Results: The more than half of tumor (55.1%) had a non-ellipsoid configuration. The predictions for outcome were consistent between two volume groups, with overall survival of 93.6% and 87.7% for small tumor (<20 mL), 62.9% and 69.1% for intermediate-size tumor (20-39 mL), and 14.5% and 16.7% for large tumors (${\geq}$40 mL) using ROI and diameter based measurement, respectively. Disease-free survival was 93.8% and 90.6% for small tumor, 54.3% and 62.7% for intermediate-size tumor, and 13.7% and 10.3% for large tumor using ROI and diameter based method, respectively. Differences in outcome between size groups were statistically significant, and the differences in outcome predicted by the tumor volume by two different methods. Conclusion: Our data suggested that large numbers of cervical cancers are not ellipsoid. However, simple diameter-based tumor volume measurement appears to be useful in comparison with ROI-based volumetry for predicting outcome in cervical cancer patients.

• Toxicological Research
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• v.33 no.3
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• pp.173-182
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• 2017

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.430-436
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• 2018

Objective: The purpose of this study was to evaluate whether or not the d-dimer level indicating hyperfibrinolysis could be a predictor of early poor outcome (massive transfusion, death within 24 hours) associated with trauma-induced coagulopathy in blunt trauma without significant brain injury. Methods: This study was a retrospective observational study using 516 blunt trauma patients without significant brain injury. The poor outcome group, including patients receiving massive transfusion and those who died within 24 hours, consisted of 33 patients (6.4%). The variables were compared between the poor outcome group and good outcome group, and logistic regression analysis was performed using statistically significant variables. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the poor outcome prediction ability of the initial d-dimer level. Results: The poor outcome group showed more serious anatomical, physiological, and laboratory data than the good outcome group. In the ROC curve analysis for evaluation of the poor outcome prediction of the d-dimer level, the area under the curve value was 0.87 (95% confidence interval [CI], 0.84-0.90) while the cut-off value was 27.35 mg/L. In the logistic regression analysis, the high d-dimer level was shown to be an independent predictor of poor outcome (adjusted odds ratio, 14.87; 95% CI, 2.96-74.67). Conclusion: The high d-dimer level (>27.35 mg/L) can be used as a predictor for the poor outcome of patients with blunt trauma without significant brain injury.

• Journal of Korean Neurosurgical Society
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• v.30 no.8
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• pp.970-975
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• 2001

Objective : By conducing a review of clinical outcomes for patients with aneurysm treated using current microneurosurgical techniques and intensive care unit management, we speculated that grading systems based only on clinical condition or CT finding after admission failed to provide a significant stratification of outcome between individual grades of patients, because these systems did not include the factor for postoperative vasospasm. We hypothesized that postoperative blood flow velocity could have a significant impact on outcome prediction for patients surgically treated for intracranial aneurysms. Methods : We conducted a analysis on patient- and lesion-specific factors that might have been associated with outcome in a series of 55 aneurysm operations performed with measurements of blood-flow velocity with transcranial Doppler ultrasonography(TCD). In the new scale(NS) score system, 1 point is assigned additionally for the case with Hunt and Hess(H-H)/World Federation of Neurological Surgeons(WFNS) Grade IV or V, Fisher Scale(FS) score 3 or 4, aneurysm size greater than 10mm, patient age older than 60 years, blood-flow velocity higher than 120cm/sec, and posterior circulation lesion. By adding the total points, a 6-point scale score(score 0-6) is obtained. Results : Age of patient, size of aneurysm, clinical condition(H-H grade and WFNS), FS score, and blood flow velocity(TCD 1day after operation) were independently and strongly associated with long-term outcome. When NS scores were applied to 55 patients with at least 6 months follow-up, the correlation of individual scores with outcome was strongly validated the retrospective findings. Conclusion : It was speculated that TCD could be used to assess postoperative vasospasm and to monitor noninvasively the patients with aneurysmal SAH. This NS score system is easy to apply, divide patients into groups with different outcome, and is comprehensive, allowing for more accurate prediction of surgical outcome.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• BMB Reports
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• v.43 no.12
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• pp.836-841
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• 2010

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = $2.90{\times}10^{-4}$) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.141-158
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• 2019

Objectives: An adverse outcome pathway is a biological pathway that disturbs homeostasis and causes toxicity. It is a conceptual framework for organizing existing biological knowledge and consists of the molecular initiating event, key event, and adverse output. The AOP concept provides intuitive risk identification that can be helpful in evaluating the carcinogenicity of chemicals and in the prevention of cancer through the assessment of chemical carcinogenicity predictions. Methods: We reviewed various papers and books related to the application of AOPs for the prevention of occupational cancer. We mainly used the internet to search for the necessary research data and information, such as via Google scholar(http://scholar.google.com), ScienceDirect(www.sciencedirect.com), Scopus(www.scopus. com), NDSL(http: //www.ndsl.kr/index.do) and PubMed(http://www.ncbi.nlm.nih.gov/pubmed). The key terms searched were "adverse outcome pathway," "toxicology," "risk assessment," "human exposure," "worker," "nanoparticle," "applications," and "occupational safety and health," among others. Results: Since it focused on the current state of AOP for the prediction of toxicity from chemical exposure at work and prospects for industrial health in the context of the AOP concept, respiratory and nanomaterial hazard assessments. AOP provides an intuitive understanding of the toxicity of chemicals as a conceptual means, and it works toward accurately predicting chemical toxicity. The AOP technique has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment. AOP can be applied to the assessment of chemical carcinogenicity along with efforts to understand the effects of chronic toxic chemicals in workplaces. Based on these predictive tools, it could be possible to bring about a breakthrough in the prevention of occupational and environmental cancer. Conclusions: The AOP tool has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment and has been widely used in the field of chemical risk assessment and the evaluation of carcinogenicity at work. It will be a useful tool for prediction, and it is possible that it can help bring about a breakthrough in the prevention of occupational and environmental cancer.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.741-751
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• 2000

To achieve high performance by exploiting instruction level parallelism aggressively in superscalar processors, it is necessary to overcome the limitation imposed by control dependences and data dependences which prevent instructions from executing parallel. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively its data dependent instruction based on the predicted outcome. In this paper, a hybrid value prediction scheme with dynamic classification mechanism is proposed. We design a hybrid predictor by combining the last predictor, a stride predictor and a two-level predictor. The choice of a predictor for each instruction is determined by a dynamic classification mechanism. This makes each predictor utilized more efficiently than the hybrid predictor without dynamic classification mechanism. To show performance improvements of our scheme, we simulate the SPECint95 benchmark set by using execution-driven simulator. The results show that our scheme effect reduce of 45% hardware cost and 16% prediction accuracy improvements comparing with the conventional hybrid prediction scheme and two-level value prediction scheme.