Haemoglobin response modelling under erythropoietin treatment: Physiological model-informed machine learning method

Patients with renal anaemia are usually treated with recombinant human erythropoietin (EPO) because of insufficient renal EPO secretion. The establishment of a good haemoglobin (Hgb) response model is a necessary condition for dose optimization design. The purpose of this paper is to apply physics-informed neural networks (PINN) to build the Hgb response model under EPO treatment. Neural network training is guided by a physiological model to avoid overfitting problems. During the training process, the parameters of the physiological model can be estimated simultaneously. To handle differential equations with impulse inputs and time delays, we propose approximate model equations for the pharmacokinetic (PK) model and the pharmacodynamic (PD) model, respectively. The modified PK/PD model was incorporated into PINN for training. Tests on simulated data and clinical data show that the proposed method has better performance than data-driven modelling methods and the traditional physiological modelling based on the least squares method.