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从血液动力学模拟中挖掘数据以生成预测和解释模型。

Mining data from hemodynamic simulations for generating prediction and explanation models.

作者信息

Bosnić Zoran, Vračar Petar, Radović Milos D, Devedžić Goran, Filipović Nenad D, Kononenko Igor

机构信息

University of Ljubljana, Faculty of Computer and Information Science.

出版信息

IEEE Trans Inf Technol Biomed. 2012 Mar;16(2):248-54. doi: 10.1109/TITB.2011.2164546. Epub 2011 Aug 15.

DOI:10.1109/TITB.2011.2164546
PMID:21846607
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.

摘要

人类死亡的最常见原因之一是中风,它可能由颈动脉分叉狭窄引起。在我们的工作中,我们旨在提出一个医学专家系统的原型,该系统可以显著帮助医学专家检测血流动力学异常(动脉壁剪切应力增加)。基于获取的模拟数据,我们应用了几种方法来:1)预测动脉中最大壁面剪切应力的大小和位置,2)估计计算预测的可靠性,3)为模型的决策提供用户友好的解释。所得结果表明,所评估的方法可以为给定的问题领域提供一个有用的工具。

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