REINFORCEMENT LEARNING FOR ADAPTIVE HEALTHCARE DECISION SUPPORT SYSTEMS

• Published in: Informing science Vol. 28; p. 1
• Main Authors: P., Sreekala S, Saxena, Mudit, S., Revathy, M., Rajapriya, S., Shanthi N, S., Saravanakumar
• Format: Journal Article
• Language: English
• Published: Informing Science Institute 01.01.2025
• Subjects: Data mining; Decision support systems; Decision-making; Medical records; India
• ISSN: 1547-9684

Aim/Purpose The aim of this work is to propose an RL framework in healthcare settings for adaptive healthcare decision-aid strategy. Background Adaptive decision guide systems are needed to assist doctors in making timely and accurate selections because healthcare environments are getting more complicated and variable. But, because of the enormous stakes and the need for interpretability and dependability in selection-making, using Reinforcement Learning (RL) in healthcare environments brings a unique set of difficulties. Methodology The RL framework trains an agent using patient records, clinical guidelines, and expert knowledge. The agent interacts with healthcare settings, which can be both simulated or natural, and gets input on how its decisions affect the outcomes. The framework incorporates clear methods for decision-making and limitations on the actions the RL agent can undertake to guarantee both safety and clarity. Contribution An RL framework in healthcare settings is proposed in cope painting for adaptive healthcare decision aid strategy, which can learn the excellent choice policies from affected person facts and yet assure protection, interpretability, and medical relevance. Findings The findings of the experimental evaluations show that the RL framework works nicely to improve choice-making accuracy and versatility for a long time. Patient results can be substantially improved using the device while following medical recommendations and safety policies. Recommendations for Researchers To integrate the device into medical exercise because clinicians can recognize and trust the suggestions made with the aid of the gadget due to the fact the learned decision rules are interpretable. Future Research It can be enhanced using several deep-learning algorithms to achieve better accuracy and performance. Keywords adaptive decision support systems, reinforcement learning, interpretability, healthcare, patient outcomes