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
小叽咕发布了新的文献求助10
1秒前
炙热的小小完成签到 ,获得积分10
1秒前
阿萨十大发布了新的文献求助10
1秒前
1秒前
2秒前
STUBLE发布了新的文献求助10
5秒前
洁净的寻菱完成签到 ,获得积分10
6秒前
6秒前
郭子啊完成签到 ,获得积分10
9秒前
yan发布了新的文献求助10
9秒前
yadan完成签到,获得积分10
10秒前
悲伤土豆发布了新的文献求助10
12秒前
12秒前
一一一完成签到 ,获得积分10
13秒前
13秒前
科研通AI6.4应助缓慢从波采纳,获得10
17秒前
落寞代亦发布了新的文献求助10
19秒前
尊敬兔子完成签到,获得积分10
19秒前
22秒前
今后应助STUBLE采纳,获得10
24秒前
ttiod发布了新的文献求助10
24秒前
25秒前
FashionBoy应助二三十采纳,获得10
26秒前
AST发布了新的文献求助20
26秒前
SSSsss发布了新的文献求助10
26秒前
落寞代亦完成签到,获得积分10
28秒前
28秒前
28秒前
腼腆的之槐应助悲伤土豆采纳,获得10
30秒前
若回首发布了新的文献求助10
30秒前
大白兔完成签到 ,获得积分10
32秒前
33秒前
36秒前
37秒前
39秒前
学习完成签到,获得积分10
41秒前
小半完成签到,获得积分10
41秒前
吉吉国王完成签到 ,获得积分10
41秒前
张杰发布了新的文献求助10
43秒前
默默千亦完成签到 ,获得积分10
43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
Markov Chain Monte Carlo 5000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Evidence Summary. Injection (subcutaneous):op- timal administration 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
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 7494612
求助须知:如何正确求助?哪些是违规求助? 9085949
关于积分的说明 19378263
捐赠科研通 7106387
什么是DOI,文献DOI怎么找? 3249772
关于科研通互助平台的介绍 2419161
邀请新用户注册赠送积分活动 2235473