Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare
Interventions
- URL: http://arxiv.org/abs/2309.10980v3
- Date: Wed, 7 Feb 2024 12:12:40 GMT
- Title: Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare
Interventions
- Authors: Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, and
Jianming Yong
- Abstract summary: 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.
We evaluate the performance of the proposed multi-agent DRL framework using real-world physiological and motion data from two datasets.
- Score: 14.79027898310755
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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