Adaptive Individualized Modeling From Limited Clinical Data for Precise Anemia Management

It is challenging in practice to achieve a steady-state value for external human recombinant erythropoietin (EPO) dosage to be administrated to maintain Hemoglobin (Hb) level within the desired range of 11–12 g/dl based on population-based models for anemia management due to inter-and intra-variability of the patients. On the other hand, Pharmacokinetic (PK) and Pharmacodynamic (PD) characteristics can vary for the patients over the course of treatment due to aging and other life changes. To address the inter-and intra-variability in anemia management, the semi-blind robust identification approach is proposed to obtain individualized patient models using limited number of clinical patient data. Semi-blind robust identification utilizes the effect of the initial condition during system identification to reduce the identification error. To reflect the patient’s true dose-response relation as time passes and ensure the suitability of the individualized model for the controller, the model (In)validation technique is discussed to provide appropriate mathematical evidence about the suitability of the individualized model for dose prediction and controller design via testing it on new clinical data of the particular patient. One-step-ahead prediction results are shown for identified individualized patient models. The individualized patient models provide decision support to the clinicians about EPO dosage to avoid undershoot or overshoot of Hb level. Minimum mean squared error (MMSE) is calculated for the predicted values obtained by the models identified using semi-blind robust identification with and without the model (In)validation against clinically acquired EPO-Hb data.