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Possibilities of Neural Networks for Personalization Approaches for Prevention of Complications After Endovascular Interventions

机译：神经网络用于个性化治疗血管内介入术后并发症的可能性

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It is known that most of the diseases of the cardiovascular system are accompanied by disorders in the hemostatic system. The hemostatic system is one of the most complex systems. It has a hierarchical structure with a plurality of components. We analyze the results of thrombin generation test (TGT) which allows of estimating the actions of all components of the hemostatic system. The problem is complicated by the presence of too many various clinical cases. The simple statistical methods do not provide global assessments. We suggest the universal neural network approach for building hemostatic system models based on the factors which don't have a statistically significant difference for various types of clinical post surgery cases. The neural network instruments allow of taking into account the nonlinear hierarchical nature of considered system and building individual models for each clinical cases. The aim of our study is to develop the neural network hemostatic system model for forecasting of disease progression and complications after endovascular interventions.
机译：众所周知，心血管系统的大多数疾病都伴随着止血系统的失调。止血系统是最复杂的系统之一。它具有包含多个组件的层次结构。我们分析凝血酶生成测试（TGT）的结果，该结果可以估算止血系统所有组件的作用。由于存在太多各种临床病例，使问题变得复杂。简单的统计方法不提供全局评估。我们建议使用通用神经网络方法来建立止血系统模型，其依据的因素对于各种类型的术后临床病例没有统计学上的显着差异。神经网络仪器可以考虑到所考虑系统的非线性层次性质，并为每个临床病例建立单独的模型。我们研究的目的是开发神经网络止血系统模型，以预测血管内干预后的疾病进展和并发症。

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