ToPP: Tumor online Prognostic Analysis Platform for Prognostic Feature Selection and Clinical Patient Subgroup Selection

2021 ◽  
Author(s):  
Jian Ouyang ◽  
Guangrong Qin ◽  
Zhenhao Liu ◽  
Xingxing Jian ◽  
Tieliu Shi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Patient Subgroup ◽  
Clinical Patient ◽  
Subgroup Selection ◽  
Prognostic Feature

Abstract WP25: A Novel Adaptive Trial Design Increases the Power of Endovascular Stroke Studies

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Maarten Lansberg ◽  
Ninad Bhat ◽  
Joseph P Broderick ◽  
Yuko Y Palesch ◽  
Philip W Lavori ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Interim Analysis ◽  
Trial Design ◽  
Adaptive Design ◽  
Type I ◽  
Vessel Occlusion ◽  
Patient Subgroup ◽  
Traditional Design ◽  
Subgroup Selection ◽  
Adaptive Trial

Introduction: It is difficult to choose trial enrollment criteria that will yield a robust treatment effect. To address this problem, we developed a novel trial design that restricts enrollment criteria to the patient subgroup most likely to show benefit, if an interim analysis indicates futility in the overall sample. Future recruitment, and the population in which the primary hypothesis is tested, is limited to the selected subgroup. Hypothesis: A design with adaptive subgroup selection increases the power of endovascular stroke studies. Methods: We ran simulations to compare the power of the adaptive design with that of a traditional design. Trial parameters were: type I error 0.025, type II error 0.1, analysis after 450, 675 and 900 patients (interim and final analyses in IMS III). Outcome data were based on 90 day mRS scores observed in IMS III among patients with a vessel occlusion on baseline CTA (n=289). Subgroups were defined a priori according to vessel occlusion (ICA ± distal occlusion vs M1 vs M2-4), onset-to-randomization time (early vs late), and treatment allocation (IA+IV vs IV alone). The treatment effect in the overall cohort was a mean mRS improvement of 0.15 (2.41 for IV+IA vs 2.56 for IV alone; SD 1.45). The subgroup treatment effects were: early ICA = 0.54, late ICA = 0.60, early M1 = 0.33, late M1 = 0.07, early M2-4 = -0.66, and late M2-4 = -0.35. Results: The traditional design showed a treatment benefit in 31% of simulations. The adaptive design showed benefit in 91%, failed to show benefit after enrollment of the maximum sample in 1%, and stopped early for futility in 8% of simulations. The adaptive trial stopped early for benefit in 84% of simulations. Due to early stopping, the mean number of patients randomized is 590±140 with the adaptive design vs 900 with a traditional design. Of the adaptive trial simulations that showed benefit, 91% occur after subgroup selection. The subgroup selected most often (31% of all simulations) includes early and late ICA patients. Conclusions: A trial with adaptive subgroup selection can efficiently test the effect of endovascular stroke treatment. Simulations suggest that with this design, IMS III would have 91% power and would typically stop early after interim analysis shows benefit in a patient subgroup.

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

2012 ◽  
Vol 19 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Muhammad Ahmad ◽  
Syungyoung Lee ◽  
Ihsan Ul Haq ◽  
Qaisar Mushtaq
Keyword(s):  
Feature Selection ◽  
3D Image ◽  
Image Cube

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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