Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation Models

被引：12

作者：

Bosnic, Zoran ^{[1
]}

Vracar, Petar ^{[1
]}

Radovic, Milos D. ^{[2
]}

Devedzic, Goran ^{[3
]}

Filipovic, Nenad D. ^{[3
]}

Kononenko, Igor ^{[1
]}

机构：

[1] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana 61000, Slovenia

[2] Res & Dev Ctr Bioengn BioIRC, Kragujevac, Serbia

[3] Fac Mech Engn, Kragujevac, Serbia

来源：

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE | 2012年 / 16卷 / 02期

关键词：

Arterial stenosis; data mining; machine learning; medical expert system; CAROTID BIFURCATION; INDIVIDUAL CLASSIFICATIONS; BLOOD-FLOW; RELIABILITY; RULES;

D O I：

10.1109/TITB.2011.2164546

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 for 1) 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.

引用

页码：248 / 254

页数：7

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