  Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney
disease (CKD) require a drug management system that ensures a stable and robust
output of the patient's condition in response to drug dosage. In the case of
CKD, the patients suffer from the deficiency of red blood cell count and
external human recombinant erythropoietin (EPO) is required to maintain healthy
levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD.
For an efficient and robust anemia management system for CKD patients instead
of traditional population-based approaches, individualized patient-specific
approaches are needed. Hence, individualized system (patient) models for
patient-specific drug-dose responses are required. In this research, system
identification for CKD is performed for individual patients. For
control-oriented system identification, two robust identification techniques
are applied: (1) l1 robust identification considering zero initial conditions
and (2) semi-blind robust system identification considering non-zero initial
conditions. The EPO data of patients are used as the input and Hb data is used
as the output of the system. For this study, individualized patient models are
developed by using patient-specific data. The ARX one-step-ahead prediction
technique is used for model validation at real patient data. The performance of
these two techniques is compared by calculating minimum means square error
(MMSE). By comparison, we show that the semi-blind robust identification
technique gives better results as compared to l1 robust identification.
