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' behaviour 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.

翻译：有效的患者监测对于及时干预和改善医疗结果至关重要。传统监测系统往往难以处理复杂、动态的环境以及波动的生命体征，导致识别危重状况出现延迟。针对这一挑战，我们提出了一种新颖的基于人工智能的患者监测框架，采用多智能体深度强化学习。该方法部署多个学习智能体，每个智能体专门监测特定的生理特征，如心率、呼吸和体温。这些智能体与通用医疗监测环境交互，学习患者的行为模式，并基于估计的紧急程度做出知情决策，以通知相应的医疗急救团队。在本研究中，我们使用来自两个数据集（PPG-DaLiA和WESAD）的真实生理和运动数据评估所提出的多智能体深度强化学习框架的性能。我们将结果与多个基线模型（包括Q-Learning、PPO、Actor-Critic、Double DQN和DDPG）以及监测框架（如WISEML和CA-MAQL）进行比较。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，实现了更准确的患者生命体征监测。此外，我们进行超参数优化以微调每个智能体的学习过程。通过优化超参数，我们提高了学习率和折扣因子，从而提升了智能体在监测患者健康状况方面的整体性能。