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Robust System Identification for Anemia Management

机译：贫血管理系统识别

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摘要

Anemia is the condition in which patients suffer from the deficiency of the red blood cells to carry the oxygen in the body. One of the many causes of anemia is chronic kidney disease (CKD). CKD is a disease in which kidneys are partially or completely damaged, which results in a deficiency of the oxygen-carrying red blood cells. CKD is common in older people, and external human recombinant erythropoietin (EPO) is required to maintain healthy levels of hemoglobin (Hb). In order to effectively address the impact of inter and intra-individual variability in dose-response characteristics in CKD patients, individualized patient-specific models are required instead of traditional population-based models. In this research, individualized patient models are developed by using patient-specific time-domain data with robust system identification techniques. For control-oriented system identification, two robust identification techniques are investigated: (1) l1robust identification considering zero initial conditions and (2) Semi-blind robust system identification considering non-zero initial conditions. The performance of these two techniques is compared and it is shown that the Semi-blind robust identification technique gives better results as compared to l1robust identification.
机译：贫血是患者患有红细胞缺乏的病症，以携带身体中的氧气。贫血的许多原因之一是慢性肾病（CKD）。 CKD是一种疾病，其中肾脏部分或完全损坏，这导致携带含氧红细胞的缺乏。 CKD在老年人中是常见的，并且需要外部人重组促红细胞生成素（EPO）以维持血红蛋白（HB）的健康水平。为了有效地解决CKD患者剂量反应特性的互联和胞内变异性的影响，需要个性化患者特异性模型而不是传统的基于人口的模型。在该研究中，通过使用具有鲁棒系统识别技术的患者特定的时域数据开发个性化患者模型。对于面向控制的系统识别，研究了两种稳健的识别技术：（1）考虑到零初始条件和（2）考虑非零初始条件的半盲鲁棒系统识别。比较这两种技术的性能，结果表明，与L1RoSRoS识别相比，半盲鲁棒识别技术具有更好的结果。

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