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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hyf发布了新的文献求助20
1秒前
李爱国应助wch采纳,获得10
1秒前
碧蓝翅膀发布了新的文献求助10
1秒前
3秒前
bare发布了新的文献求助10
4秒前
lulala完成签到,获得积分10
4秒前
50202发布了新的文献求助10
4秒前
lixin完成签到,获得积分10
4秒前
一自文又欠完成签到 ,获得积分10
5秒前
紫蕊傲客完成签到 ,获得积分20
6秒前
8秒前
归尘发布了新的文献求助10
10秒前
可爱的函函应助马薄函采纳,获得10
10秒前
紫蕊傲客关注了科研通微信公众号
10秒前
11秒前
支寄灵完成签到,获得积分10
11秒前
酷波er应助echo采纳,获得10
11秒前
坚定的冰淇淋完成签到,获得积分10
11秒前
dafa6f6发布了新的文献求助10
12秒前
13秒前
王红玉发布了新的文献求助10
15秒前
HPP123发布了新的文献求助10
15秒前
许可证完成签到,获得积分10
16秒前
小手冰凉完成签到,获得积分10
16秒前
liguangfei发布了新的文献求助10
17秒前
18秒前
Lucas应助hh采纳,获得10
18秒前
18秒前
科研通AI6.3应助英俊qiang采纳,获得10
19秒前
19秒前
猫猫侠发布了新的文献求助10
20秒前
22秒前
23秒前
23秒前
23秒前
25秒前
25秒前
26秒前
26秒前
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Cronologia da história de Macau 5000
Petrology and Plate Tectonics 800
Electrode Potentials 550
Matrix Methods in Data Mining and Pattern Recognition 510
Association of Reentry Well-Being with Psychological Distress, Employment, and Housing Instability 15-Months After Incarceration 500
Trees of tropical Asia : an illustrated guide to diversity 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7027809
求助须知:如何正确求助?哪些是违规求助? 8698130
关于积分的说明 18429978
捐赠科研通 6527284
什么是DOI,文献DOI怎么找? 3111538
关于科研通互助平台的介绍 2188670
邀请新用户注册赠送积分活动 2087092