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
XxxxxtPuCO发布了新的文献求助10
1秒前
迷人念柏完成签到,获得积分10
1秒前
tsunami完成签到,获得积分10
3秒前
Pan完成签到 ,获得积分10
4秒前
张欢馨应助lily采纳,获得10
5秒前
端庄的傲易完成签到 ,获得积分20
7秒前
7秒前
搜集达人应助王晨旭采纳,获得10
7秒前
7秒前
科目三应助尧九采纳,获得10
10秒前
过时的沛槐完成签到,获得积分10
11秒前
luo完成签到,获得积分10
12秒前
菜鸟学习发布了新的文献求助10
12秒前
12秒前
14秒前
14秒前
ZJK完成签到,获得积分10
17秒前
生产队的LV完成签到 ,获得积分10
17秒前
17秒前
17秒前
听听完成签到,获得积分10
18秒前
FashionBoy应助NEXT采纳,获得10
18秒前
科研通AI6.4应助XxxxxtPuCO采纳,获得10
18秒前
Xxxy发布了新的文献求助10
20秒前
24p0发布了新的文献求助10
20秒前
guzhfia发布了新的文献求助10
24秒前
Owen应助王晨旭采纳,获得10
25秒前
传奇3应助小o采纳,获得10
25秒前
26秒前
28秒前
GLORIA发布了新的文献求助20
30秒前
30秒前
chengzi完成签到,获得积分10
32秒前
dinghui完成签到,获得积分10
33秒前
33秒前
molihuakai应助Lotus采纳,获得10
35秒前
无衷应助Fourier采纳,获得10
35秒前
36秒前
hdd完成签到,获得积分10
36秒前
天晴应助慈祥的大船采纳,获得10
37秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 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
The Redesign of International Investment Contracts 600
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 7539017
求助须知:如何正确求助?哪些是违规求助? 9123656
关于积分的说明 19490828
捐赠科研通 7136330
什么是DOI,文献DOI怎么找? 3257849
关于科研通互助平台的介绍 2425138
邀请新用户注册赠送积分活动 2245912