Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution
Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution
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DOI：
10.1007/s00397-023-01402-2
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发表时间：
2023-06-27
期刊：
影响因子：
2.3
通讯作者：
Beris，Antony N. N.
中科院分区：
文献类型：
--
作者：
Farrington，Sean;Jariwala，Soham;Beris，Antony N. N.
血液流变学是研究血液流动及其所涉及的机械应力和运动学。Casson本构方程是一种流行而简单的模型，用于描述血液的稳定剪切流变，只有两个参数指定无限剪切粘度和屈服应力取决于血液生理学。先前的文献已经确定红细胞压积和纤维蛋白原浓度是影响血流的两个最重要的生理因素，但由于使用非标准化的数据集，Casson模型的先前参数化可能不可靠。本研究使用机器学习和最大的标准化数据集来改进健康个体关于红细胞压积和纤维蛋白原浓度的Casson模型的参数化。该研究还使用机器学习来识别可能影响血液流变学的潜在附加因素，即平均红细胞血红蛋白（MCH）。提出的方法证明了机器学习在改善生理学和血液流变学之间的联系方面的潜力，并可能对心血管诊断产生影响。
Hemorheology is the study of blood flow and the mechanical stresses and kinematics involved. The Casson constitutive equation is a popular and simple model used to describe the steady shear rheology of blood, with only two parameters that specify an infinite shear viscosity and a yield stress that depend on blood physiology. Previous literature has identified hematocrit and fibrinogen concentration as the two most important physiological factors that affect blood flow, but previous parameterizations of the Casson model may not be reliable due to the use of non-standardized data sets. This study uses machine learning and the largest standardized dataset to improve the parameterization of the Casson model with respect to hematocrit and fibrinogen concentration for healthy individuals. The study also employs machine learning to identify a potential additional factor, the mean corpuscular hemoglobin (MCH), that may affect blood rheology. The proposed approach demonstrates the potential for machine learning to improve the connection between physiology and blood rheology with possible implications in cardiovascular diagnostics.