title: Mining data from hemodynamic simulations for generating prediction and explanation models.
abstract: One of the most common causes of human death is stroke, which can be caused by carotid bifurcation stenosis. In our work, we aim at proposing a prototype of a medical expert system that could significantly aid medical experts to detect hemodynamic abnormalities (increased artery wall shear stress). Based on the acquired simulated data, we apply several methodologies for1) predicting magnitudes and locations of maximum wall shear stress in the artery, 2) estimating reliability of computed predictions, and 3) providing user-friendly explanation of the model's decision. The obtained results indicate that the evaluated methodologies can provide a useful tool for the given problem domain.
authors.affiliation: University of Ljubljana, Faculty of Computer and Information Science. zoran.bosnic@fri.uni-lj.si
authors.forename: Zoran
authors.initials: Z
authors.lastname: Bosnić
authors.forename: Petar
authors.initials: P
authors.lastname: Vračar
authors.forename: Milos D
authors.initials: MD
authors.lastname: Radović
authors.forename: Goran
authors.initials: G
authors.lastname: Devedžić
authors.forename: Nenad D
authors.initials: ND
authors.lastname: Filipović
authors.forename: Igor
authors.initials: I
authors.lastname: Kononenko
mesh_terms: D002339:Carotid Arteries / Q000473:pathology / Q000503:physiopathology; D016893:Carotid Stenosis / Q000473:pathology / Q000503:physiopathology*; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012044:Regression Analysis; D015203:Reproducibility of Results