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Adaptive closed‐loop resuscitation controllers for hemorrhagic shock resuscitation

复苏 控制器(灌溉) 计算机科学 自适应控制 医学 控制(管理) 急诊医学 人工智能 农学 生物
作者
Saul J. Vega,David Berard,Guy Avital,Evan Ross,Eric J. Snider
出处
期刊:Transfusion [Wiley]
卷期号:63 (S3) 被引量:5
标识
DOI:10.1111/trf.17377
摘要

After hemorrhage control, fluid resuscitation is the most important intervention for hemorrhage. Even skilled providers can find resuscitation challenging to manage, especially when multiple patients require care. In the future, attention-demanding medical tasks like fluid resuscitation for hemorrhage patients may be reassigned to autonomous medical systems when availability of skilled human providers is limited, such as in austere military settings and mass casualty incidents. Central to this endeavor is the development and optimization of control architectures for physiological closed-loop control systems (PCLCs). PCLCs can take many forms, from simple table look-up methods to widely used proportional-integral-derivative or fuzzy-logic control theory. Here, we describe the design and optimization of multiple adaptive resuscitation controllers (ARCs) that we have purpose-built for the resuscitation of hemorrhaging patients.Three ARC designs were evaluated that measured pressure-volume responsiveness using different methodologies during resuscitation from which adapted infusion rates were calculated. These controllers were adaptive in that they estimated required infusion flow rates based on measured volume responsiveness. A previously developed hardware-in-loop test platform was used to evaluate the ARCs implementations across several hemorrhage scenarios.After optimization, we found that our purpose-built controllers outperformed traditional control system architecture as embodied in our previously developed dual-input fuzzy-logic controller.Future efforts will focus on engineering our purpose-built control systems to be robust to noise in the physiological signal coming to the controller from the patient as well as testing controller performance across a range of test scenarios and in vivo.

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