A planning quality evaluation tool for prostate adaptive IMRT based on machine learning

2011 ◽  
Vol 38 (2) ◽  
pp. 719-726 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Yaorong Ge ◽  
Taoran Li ◽  
Danthai Thongphiew ◽  
Fang-Fang Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

Response to “Comment on ‘A planning quality evaluation tool for prostate adaptive IMRT based on machine learning’ ” [Med. Phys. 38, 719 (2011)]

2011 ◽  
Vol 38 (5) ◽  
pp. 2821-2821 ◽  
Author(s):  
Xiaofeng Zhu ◽  
Taoran Li ◽  
Fang-Fang Yin ◽  
Q Jackie Wu ◽  
Yaorong Ge
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

TU-E-BRB-03: A Planning Quality Evaluation Tool for Adaptive IMRT Treatment Based on Machine Learning

2010 ◽  
Vol 37 (6Part27) ◽  
pp. 3400-3400 ◽  
Author(s):  
X Zhu ◽  
T Li ◽  
D Thongphiew ◽  
Y Ge ◽  
F Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

Comment on “A planning quality evaluation tool for prostate adaptive IMRT based on machine learning” [Med. Phys. 38, 719 (2011)]

2011 ◽  
Vol 38 (5) ◽  
pp. 2820-2820 ◽  
Author(s):  
Michael Kazhdan ◽  
Todd McNutt ◽  
Russell Taylor ◽  
Binbin Wu ◽  
Patricio Simari
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Evaluation Tool

scholarly journals The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Yuna Shin ◽  
Heesuk Lee ◽  
Young-Joo Lee ◽  
Dae Keun Seo ◽  
Bomi Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Variable Selection ◽  
Roc Curve ◽  
Model Evaluation ◽  
Selection Method ◽  
The Other ◽  
Evaluation Tool ◽  
Evaluation Tools ◽  
Variable Selection Method ◽  
Boruta Algorithm

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

scholarly journals Quality Evaluation Tool for Clinician Online Continuing Medical Education

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Brittany Rosen ◽  
Gary Kreps ◽  
James M Bishop ◽  
Skye L McDonald
Keyword(s):  
Medical Education ◽  
Continuing Medical Education ◽  
Quality Evaluation ◽  
Evaluation Tool

scholarly journals CoverView: a sequence quality evaluation tool for next generation sequencing data

2018 ◽  
Vol 3 ◽  
pp. 36 ◽  
Author(s):  
Márton Münz ◽  
Shazia Mahamdallie ◽  
Shawn Yost ◽  
Andrew Rimmer ◽  
Emma Poyastro-Pearson ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Quality Evaluation ◽  
Reference Sample ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Evaluation Tool ◽  
Link Type ◽  
Generation Sequencing

Quality assurance and quality control are essential for robust next generation sequencing (NGS). Here we present CoverView, a fast, flexible, user-friendly quality evaluation tool for NGS data. CoverView processes mapped sequencing reads and user-specified regions to report depth of coverage, base and mapping quality metrics with increasing levels of detail from a chromosome-level summary to per-base profiles. CoverView can flag regions that do not fulfil user-specified quality requirements, allowing suboptimal data to be systematically and automatically presented for review. It also provides an interactive graphical user interface (GUI) that can be opened in a web browser and allows intuitive exploration of results. We have integrated CoverView into our accredited clinical cancer predisposition gene testing laboratory that uses the TruSight Cancer Panel (TSCP). CoverView has been invaluable for optimisation and quality control of our testing pipeline, providing transparent, consistent quality metric information and automatic flagging of regions that fall below quality thresholds. We demonstrate this utility with TSCP data from the Genome in a Bottle reference sample, which CoverView analysed in 13 seconds. CoverView uses data routinely generated by NGS pipelines, reads standard input formats, and rapidly creates easy-to-parse output text (.txt) files that are customised by a simple configuration file. CoverView can therefore be easily integrated into any NGS pipeline. CoverView and detailed documentation for its use are freely available at github.com/RahmanTeamDevelopment/CoverView/releases and www.icr.ac.uk/CoverView

Quality Evaluation Tool for Computer-and Web-Delivered Instruction

2005 ◽  
Author(s):  
Robert T. Hays ◽  
Renee J. Stout ◽  
David L. Ryan-Jones
Keyword(s):  
Quality Evaluation ◽  
Evaluation Tool
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