Transport physics‐informed reinforcement learning agents deployed in standalone infusion pumps for managing multidrug delivery in critical care

IF 6.1 2区 医学 Q1 ENGINEERING, BIOMEDICAL
V. Chandran Suja, A. L. H. S. Detry, N. M. Sims, D. E. Arney, S. Mitragotri, R. A. Peterfreund
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引用次数: 0

Abstract

Managing delivery of complex multidrug infusions in anesthesia and critical care presents a significant clinical challenge. Current practices relying on manual control of infusion pumps often result in unpredictable drug delivery profiles and dosing errors—key issues highlighted by the United States Food and Drug Administration (FDA). To address these issues, we introduce the SMART (synchronized‐pump management algorithms for reliable therapies) framework, a novel approach that leverages low Reynolds number drug transport physics and machine learning to accurately manage multidrug infusions in real‐time. SMART is activated based on the Shafer number (), a novel non‐dimensional number that quantifies the relative magnitude of a drug's therapeutic action timescale to its transport timescale within infusion manifolds. SMART is useful when , where drug transport becomes the rate limiting step in achieving the desired therapeutic effects. When activated, SMART monitors multidrug concentrations within infusion manifolds and leverages this information to perform end‐to‐end management of drug delivery using an ensemble of deterministic and deep reinforcement learning (RL) decision networks. Notably, SMART RL networks employ differentially sampled split buffer architecture that accelerates learning and improves performance by seamlessly combining deterministic predictions with RL experience during training. SMART deployed in standalone infusion pumps under simulated clinical conditions outperformed state‐of‐the‐art manual control protocols. This framework has the potential to revolutionize critical care by enhancing accuracy of medication delivery and reducing cognitive workloads. Beyond critical care, the ability to accurately manage multi‐liquid delivery via complex manifolds will have important bearings for manufacturing and process control.
运输物理信息强化学习代理部署在独立输液泵中,用于管理重症监护中的多药物输送
在麻醉和重症监护中管理复杂的多药输注是一项重大的临床挑战。目前依靠手动控制输液泵的做法经常导致不可预测的药物输送曲线和剂量错误——这是美国食品和药物管理局(FDA)强调的关键问题。为了解决这些问题,我们引入了SMART(用于可靠治疗的同步泵管理算法)框架,这是一种利用低雷诺数药物传输物理和机器学习来实时准确管理多药物输注的新方法。SMART是基于Shafer数()激活的,Shafer数是一种新的无维数,用于量化药物治疗作用时间尺度与输注流形内输送时间尺度的相对大小。当药物转运成为达到预期治疗效果的速率限制步骤时,SMART是有用的。激活后,SMART监测输液管内的多种药物浓度,并利用这些信息使用确定性和深度强化学习(RL)决策网络的集合执行端到端的药物输送管理。值得注意的是,SMART强化学习网络采用差分采样分割缓冲架构,通过在训练期间将确定性预测与强化学习经验无缝结合来加速学习并提高性能。在模拟临床条件下,SMART部署在独立输液泵中,优于最先进的手动控制协议。该框架有可能通过提高药物传递的准确性和减少认知工作量来彻底改变重症监护。在重症监护之外,通过复杂歧管精确管理多液体输送的能力将对制造和过程控制产生重要影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioengineering & Translational Medicine
Bioengineering & Translational Medicine Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
8.40
自引率
4.10%
发文量
150
审稿时长
12 weeks
期刊介绍: Bioengineering & Translational Medicine, an official, peer-reviewed online open-access journal of the American Institute of Chemical Engineers (AIChE) and the Society for Biological Engineering (SBE), focuses on how chemical and biological engineering approaches drive innovative technologies and solutions that impact clinical practice and commercial healthcare products.
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