贫血管理的半盲和l1鲁棒系统辨识
Semi-Blind and l1 Robust System Identification for Anemia Management
摘要：
癌症、糖尿病、心脏病、慢性肾病（CKD）等慢性疾病需要药物管理系统，以确保患者病情响应药物剂量的稳定和稳健输出。在CKD的情况下，患者患有红细胞计数不足，并且需要外部人重组促红细胞生成素（EPO）来维持血红蛋白（Hb）的健康水平。贫血是CKD患者的常见合并症。对于CKD患者的有效和强大的贫血管理系统，而不是传统的基于人群的方法，需要个性化的患者特异性方法。因此，需要针对患者特定的药物剂量反应的个体化系统（患者）模型。在本研究中，对个体患者进行CKD的系统识别。对于面向控制的系统辨识，应用了两种鲁棒辨识技术：（1）零初始条件下的l1鲁棒辨识和（2）非零初始条件下的半盲鲁棒系统辨识。以患者的EPO数据作为系统的输入，Hb数据作为系统的输出。对于本研究，通过使用患者特定数据开发个性化患者模型。ARX一步预测技术用于真实患者数据的模型验证。通过计算最小均方误差（MMSE）比较了这两种技术的性能。通过比较，我们表明，半盲鲁棒辨识技术给出了更好的结果相比，l1鲁棒辨识。
英文摘要：
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.
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