• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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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.

• 한국전문물리치료학회지
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• 제17권3호
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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.

• 한국환경보건학회지
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• 제50권2호
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Radiation Oncology Journal
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• 제30권2호
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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.

• Korean Journal of Radiology
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• 제20권6호
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• pp.1001-1002
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• 2019

• Korean Journal of Radiology
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• 제20권8호
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• pp.1311-1312
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• 2019

• Toxicological Research
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• 제33권3호
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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.

• 대한응급의학회지
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• 제29권5호
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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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• 제30권8호
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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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권5호
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• pp.161-168
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• 2020

스포츠 경기 결과예측은 전반적인 경기의 흐름과 승패에 영향을 미치는 변인들의 분석을 통해 팀의 전략 수립을 가능하게 해준다. 이와 같은 스포츠 경기결과 예측에 대한 연구는 주로 통계학적 기법과 기계학습 기법을 활용하여 진행되어 왔다. 승부예측 모델은 무엇보다 예측 성능이 가장 중요시된다. 그러나 최적의 성능을 보이는 예측 모델은 학습에 사용되는 데이터에 따라 다르게 나타나는 경향을 보였다. 본 논문에서는 이러한 문제를 해결하기 위해 데이터가 달라지더라도 해당 데이터에 대한 예측 시 가장 좋은 성능을 보이는 모델의 선택이 가능한 기존의 축구경기결과 예측에서 좋은 성능을 보여온 통계학적 모델과 기계학습 모델을 결합한 새로운 앙상블 모델을 제안한다. 본 논문에서 제안하는 앙상블 모델은 각 단일모델들의 경기 예측결과와 실제 경기결과를 병합한 데이터로부터 최종예측모델을 학습하여 경기 승부예측을 수행한다. 제안 모델에 대한 실험 결과, 기존 단일모델들에 비해 높은 성능을 보였다.