论文标题
联合数据插补和机械建模，用于模拟不完整数据集中的心脑相互作用
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
论文作者
论文摘要
在临床研究中使用机械模型受到代表不同解剖学和生理过程的多模式患者数据的限制。例如，神经影像学数据集并不能为心脏疾病中心血管因素建模的心脏特征提供足够的表示。为了解决这个问题，我们引入了一个概率框架，用于联合心脏数据插补和心血管机械模型的个性化，并应用于患有心脏不完整的大脑研究。我们的方法是基于一个差异框架，用于从可用功能中联合推断心脏信息的插定模型，以及可以忠实地重现个性化心血管动力学的高斯工艺模拟器。英国生物库的实验结果表明，我们的模型允许在包含最小心脏信息的数据集中准确插入缺失的心脏特征，例如收缩压和舒张血压仅具有共同估算集团模型的仿真参数。这可以通过模拟与大脑解剖学不同条件相对应的逼真的心动力学来对心脏脑关节关系进行新的探索。
The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy.
