Authors: Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Jianming Yong

Published on: September 20, 2023

Impact Score: 8.38

Arxiv code: Arxiv:2309.10980

Summary

• What is new: A novel AI-driven patient monitoring framework using multi-agent deep reinforcement learning (DRL) to monitor specific physiological features.
• Why this is important: Traditional monitoring systems fail to effectively manage dynamic environments with fluctuating vital signs, causing delays in critical condition detection.
• What the research proposes: A multi-agent DRL framework where each agent monitors a specific physiological feature and alerts Medical Emergency Teams based on the estimated level of emergency.
• Results: The proposed framework outperforms baseline models in accuracy of monitoring vital signs, with improvements through hyperparameter optimization.

Technical Details

Technological frameworks used: Multi-agent deep reinforcement learning

Models used: Q-Learning, PPO, Actor-Critic, Double DQN, DDPG, WISEML, CA-MAQL

Data used: PPG-DaLiA, WESAD

Potential Impact

Healthcare monitoring systems, Medical Emergency Teams (METs), and companies producing traditional patient monitoring technologies.

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