心血管系统的混合神经普通微分方程模型
Gevik Grigorian1, Sandip V George2, Sam Lishak3
1Department of Mechanical Engineering, University College London, WC1E 6BT London, UK.
Journal of the Royal Society, Interface
|March 19, 2024
概括
这项研究引入了一种新的混合神经普通微分方程 (ODE) 模型来模拟心血管系统 (CVS) 相互作用. 新型号准确地捕捉了心室相互作用,并且对噪声有很强的耐受性,为计算心血管研究提供了强大的工具.
科学领域:
- 计算生物学是一种计算生物学.
- 心血管生理学心血管生理学
- 医学中的人工智能.
背景情况:
- 心血管系统 (CVS) 建模对于了解心脏功能至关重要.
- 现有的计算模型经常简化复杂的心室相互作用或忽略它们.
- 在CVS模型中对非线性方程的数值近似提出了重大挑战.
研究的目的:
- 开发一种替代方法来建模心室相互作用的CVS.
- 使用混合神经普通微分方程 (ODE) 结构进行增强的建模.
- 创建心血管动态的部分学习机械模型.
主要方法:
- 使用CVS的一次性参数ODE模型模拟合成时间序列数据.
- 构建了一个混合神经ODE模型,其中神经网络控制了心室相互作用.
- 训练了混合神经ODE模型,使用合成数据与不同水平的测量噪声.
- 使用符号回归来从神经网络中提取分析表达式.
主要成果:
- 混合神经ODE模型成功捕获了心室相互作用.
- 该模型展示了良好的预测能力.
- 这种方法在测量噪声方面被证明是可靠的.
- 符号回归产生了节和可解释的分析函数.
结论:
- 拟议的混合神经ODE方法为建模复杂的心血管动态提供了可行的替代方案.
- 这种方法有效地捕捉了心室相互作用,克服了传统计算模型的局限性.
- 由此产生的部分学习的机械模型提供了关于心血管生理学的见解,并具有噪声弹性.
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