Скачать или смотреть Reinforcement Learning for Smart Hospitals Q Learning vs SARSA

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Title of the Article: Application of Q-Learning and SARSA Algorithms in Smart Hospital Management with a Value-Added Approach
Author: Hoamira Oroujzadeh
Abstract
In today’s world, the digital transformation of the healthcare system—especially within hospitals—has become a necessity. The use of reinforcement learning algorithms such as Q-Learning and SARSA plays a significant role in optimizing both clinical and operational processes. This paper presents two practical hospital-based scenarios to compare and analyze the performance of these two algorithms in creating operational and clinical value.
Introduction
Reinforcement learning (RL) algorithms enable systems to learn optimal decisions through trial and error in complex environments. In the healthcare sector, RL methods can significantly enhance operational efficiency, safety, and personalized care. This article explores two prominent RL algorithms—Q-Learning and SARSA—and evaluates their real-world applicability within smart hospital systems.
1. Q-Learning in Operating Room Scheduling
Scenario: In a high-volume hospital, the system must determine the optimal sequence of surgeries to enhance efficiency.
Objective: Reduce patient wait times and maximize the utilization of operating rooms.
Method: The Q-Learning algorithm analyzes historical data to learn which surgery sequences (e.g., starting with shorter procedures) yield the highest efficiency—even if those sequences were not previously applied.
Outcome: Reduced waiting time, increased number of surgeries performed per day, and improved utilization of resources—resulting in operational value.
2. SARSA in Smart Insulin Pump Adjustment
Scenario: A hospitalized diabetic patient is using a smart insulin pump.
Objective: Maintain stable blood glucose levels without dangerous fluctuations.
Method: SARSA evaluates the patient’s real-time response after each insulin dose and adjusts the next dose accordingly. Unlike Q-Learning, SARSA learns only from actions that were actually executed.
Outcome: Highly personalized and safer treatment, minimizing clinical risks and enhancing patient outcomes—creating clinical value.
Comparative Summary of the Two Algorithms in Smart Hospitals
Algorithm Application Scenario Focus Final Outcome
Q-Learning Operating room scheduling Efficiency and optimization Better use of hospital resources
SARSA Insulin pump dosage adjustment Safety and personalization Reduced risk and improved clinical results
Q-Learning is based on learning the best possible action—even if it wasn’t implemented—while SARSA learns only from actions that were actually taken. This fundamental difference results in different strategic applications within smart hospital systems. Together, they offer a powerful approach to optimize both resource management and patient-centered care.
Conclusion
As hospitals move toward digital transformation, selecting the right algorithm for each application becomes crucial for the success of smart healthcare initiatives. Q-Learning proves effective for operational optimization, whereas SARSA excels in ensuring safety and treatment personalization. The combined use of these algorithms provides a strategic pathway for improving service quality, enhancing hospital productivity, and minimizing medical errors.