Strategies and Considerations for Safe Reinforcement Learning in Programming Cardiac Implantable Electronic Devices.

Medical research archives Pub Date : 2025-03-01 Epub Date: 2025-03-29 DOI:10.18103/mra.v13i3.6363
John Komp, Aaptha Boggaram, David P Kao, Ashutosh Trivedi, Michael A Rosenberg
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Abstract

The programming of cardiac implantable electronic devices, such as pacemakers and implantable defibrillators, represents a promising domain for the application of automated learning systems. These systems, leveraging a type of artificial intelligence called reinforcement learning, have the potential to personalize medical treatment by adapting device settings based on an individual's physiological responses. At the core of these self-learning algorithms is the principle of balancing exploration and exploitation. Exploitation refers to the selection of device programming settings previously demonstrated to provide clinical benefit, while exploration refers to the real-time search for adjustments to device programming that could provide an improvement in clinical outcomes for each individual. Exploration is a critical component of the reinforcement learning algorithm, and provides the opportunity to identify settings that could directly benefit individual patients. However, unconstrained exploration poses risks, as an automated change in certain settings may lead to adverse clinical outcomes. To mitigate these risks, several strategies have been proposed to ensure that algorithm-driven programming changes achieve the desired level of individualized optimization without compromising patient safety. In this review, we examine the existing literature on safe reinforcement learning algorithms in automated systems and discuss their potential application to the programming of cardiac implantable electronic devices.

心脏植入式电子设备编程中安全强化学习的策略和考虑。
心脏植入式电子设备的编程,如起搏器和植入式除颤器,代表了自动学习系统应用的一个有前途的领域。这些系统利用一种被称为强化学习的人工智能,有可能根据个人的生理反应调整设备设置,从而实现个性化医疗。这些自我学习算法的核心是平衡探索和利用的原则。开发是指选择先前证明可以提供临床效益的设备编程设置,而探索是指实时搜索设备编程的调整,可以为每个人提供临床结果的改善。探索是强化学习算法的关键组成部分,并提供了识别可以直接使个体患者受益的设置的机会。然而,不受约束的探索存在风险,因为在某些设置中自动更改可能导致不良的临床结果。为了减轻这些风险,已经提出了几种策略,以确保算法驱动的编程更改在不损害患者安全的情况下达到所需的个性化优化水平。在这篇综述中,我们研究了自动化系统中安全强化学习算法的现有文献,并讨论了它们在心脏植入式电子设备编程中的潜在应用。
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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