Distinguishing Vulnerability, Prediction, and Progression in the Preschizophrenic Brain

2007 ◽  
Vol 64 (2) ◽  
pp. 250 ◽  
Author(s):  
Stephen Lawrie
Keyword(s):  
Vulnerability Prediction

scholarly journals Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaoya Guo ◽  
Akiko Maehara ◽  
Mitsuaki Matsumura ◽  
Liang Wang ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Plaque ◽  
Support Vector ◽  
Single Factor ◽  
Plaque Vulnerability ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vulnerability Prediction ◽  
Biomechanical Factors

Abstract Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

scholarly journals An outline of the method for predicting IT vulnerabilities

2018 ◽  
Vol 210 ◽  
pp. 02010 ◽  
Author(s):  
Mariusz Zieja ◽  
Mirosław Zieja ◽  
Artur Stachurski
Keyword(s):  
Method Development ◽  
Preventive Measures ◽  
Vulnerability Analysis ◽  
Internal Factors ◽  
Factors Affecting ◽  
Quantitative Models ◽  
Software Vulnerabilities ◽  
Vulnerability Prediction ◽  
The Way

Majority of the currently known quantitative models for vulnerability analysis do not allow for a comprehensive vulnerability prediction process for a selected software. The article presents the outline of the method for predicting software vulnerabilities. The presented solution is based on probabilistic properties that allow to reflect external and internal factors affecting software and determining its vulnerabilities. Also, a possible direction of further method development was described, indicating the way of improving the method with elements representing preventive measures, as a result of which it may be possible to limit or eliminate potential software vulnerabilities.

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