Effective patient monitoring is vital for timely interventions and improved healthcare outcomes. Traditional monitoring systems often struggle to handle complex, dynamic environments with fluctuating vital signs, leading to delays in identifying critical conditions. To address this challenge, we propose a novel AI-driven patient monitoring framework using multi-agent deep reinforcement learning (DRL). Our approach deploys multiple learning agents, each dedicated to monitoring a specific physiological feature, such as heart rate, respiration, and temperature. These agents interact with a generic healthcare monitoring environment, learn the patients' behavior patterns, and make informed decisions to alert the corresponding Medical Emergency Teams (METs) based on the level of emergency estimated. In this study, we evaluate the performance of the proposed multi-agent DRL framework using real-world physiological and motion data from two datasets: PPG-DaLiA and WESAD. We compare the results with several baseline models, including Q-Learning, PPO, Actor-Critic, Double DQN, and DDPG, as well as monitoring frameworks like WISEML and CA-MAQL. Our experiments demonstrate that the proposed DRL approach outperforms all other baseline models, achieving more accurate monitoring of patient's vital signs. Furthermore, we conduct hyperparameter optimization to fine-tune the learning process of each agent. By optimizing hyperparameters, we enhance the learning rate and discount factor, thereby improving the agents' overall performance in monitoring patient health status. Our AI-driven patient monitoring system offers several advantages over traditional methods, including the ability to handle complex and uncertain environments, adapt to varying patient conditions, and make real-time decisions without external supervision.

翻译：有效的患者监护对于及时干预和改善医疗结果至关重要。传统监护系统往往难以应对具有波动生命体征的复杂动态环境，导致识别危急状况出现延迟。为解决这一挑战，我们提出了一种基于多智能体深度强化学习的AI驱动患者监护新框架。该方法部署多个学习智能体，每个智能体专门监测特定生理特征（如心率、呼吸和体温）。这些智能体与通用医疗监护环境交互，学习患者行为模式，并根据评估的紧急程度作出预警决策，通知相应的医疗急救团队。本研究利用PPG-DaLiA和WESAD两个数据集中的真实生理与运动数据，评估了所提出的多智能体深度强化学习框架的性能。我们将结果与多种基线模型（包括Q-Learning、PPO、Actor-Critic、Double DQN和DDPG）以及WISEML和CA-MAQL等监护框架进行了比较。实验表明，所提出的深度强化学习方法在所有基线模型中表现最优，实现了对患者生命体征更精确的监测。此外，我们通过超参数优化对每个智能体的学习过程进行微调。通过优化学习率和折扣因子等超参数，提升了智能体监测患者健康状况的整体性能。与传统的监护方法相比，我们的AI驱动患者监护系统具有显著优势，包括能够应对复杂和不确定环境、适应不同患者状况，以及无需外部监督即可作出实时决策。