Abstract：
Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism-based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low-dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data-driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX-type functions is presented. This \"translation\" was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism-based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE-based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical-statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development.
摘要：
由于成熟过程，新生儿的血清肌酐遵循复杂的动力学，在生命的最初几周最明显。描述复杂动力学的基于机制的模型的开发需要在药物计量学（PMX）建模方面的专业知识和大量的模型开发时间。最近发布的低维神经常微分方程（NODE）的机器学习（ML）方法能够自动对来自新生儿的此类数据进行建模。然而,这种有效的数据驱动方法本身不会产生临床可解释的模型.在这项工作中,提出了一种用合理的PMX型函数推导可解释模型的方法。考虑到成熟过程和协变量，此“翻译”用于推导新生儿血清肌酐的PMX模型。将开发的模型与先前发布的基于机制的PMX模型进行了比较，而两种模型的机械结构相似。然后，考虑到胎龄和出生体重的不同协变量值，将开发的模型用于模拟生命最初几周的血清肌酐浓度。从这些模拟得到的参考血清肌酸酐值与观察到的血清肌酸酐值和先前公布的参考值一致。因此，提出的基于NODE的ML方法对新生儿复杂的血清肌酐动力学进行建模，并得出可解释的，类似于传统PMX模型的数理统计分量展示了一种新颖的，在临床环境和儿科药物开发中促进复杂动力学建模的可行方法。
