Reinforcement Learning for Real-time ICU Patient Management in Critical Care

Managing patients, especially in high-stakes environments like intensive care units (ICUs), is a difficult and time-sensitive task. Reinforcement learning, or RL, has the potential to greatly enhance the health of patients and the efficiency with which existing resources are used in the administration of real-time intensive care unit patients. To prove the superiority of the RL-based methodology, we compare the results of our RL-based method to those of more conventional methodologies. The proposed method combines the Actor-Critical architecture for continuous actions with a baseline value estimate and Q-Learning for selecting discrete actions. Policy Gradient is proposed for controlling continuous activities. Treatment choices for ICU patients can be optimized using this mix of algorithms because of their robustness and flexibility. Results from simulations reveal that the RL-based strategy consistently outperforms the traditional techniques in terms of key performance criteria such patient outcome improvement, resource efficiency, treatment response time, adverse event avoidance, patient satisfaction, and cost savings. In addition to improving patient care and healthcare resource allocation, this study demonstrates that RL has the potential to transform real-time patient management in intensive care units. Maximizing the effectiveness with which patients are cared for in the ICU is of paramount importance in the dynamic field of critical care. Using reinforcement learning (RL), this study introduces a novel approach to the treatment of ICU patients in real time. To enhance patient outcomes, resource utilization efficiency, and overall cost savings, the proposed method employs RL algorithms to encourage data-driven decision-making. We test our RL-based technique against six traditional methods and show that it outperforms them all.