학술논문

Personalized Healthcare Recommendations with Q-Learning Reinforcement Learning
Document Type
Conference
Source
2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 2023 International Conference on. 1:1-6 Dec, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Geoscience
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Industries
Ethics
Technological innovation
Q-learning
Medical services
Artificial intelligence
Guidelines
Q-Learning
Personalized Healthcare
Adaptability
Ethical Considerations
Patient Outcomes
Language
Abstract
The transforming potential of Q-Learning in customized medical recommendations is examined in this study. Its performance in comparison to conventional methods, strategic data utilization, and flexibility in a variety of scenarios all show great promise. However, a significant gap in the literature emphasizes how important it is to take ethics into account when using patient data. The findings show that Q-learning improves patient outcomes, but its ethical implications are still mostly unknown. This study offers ethical guidelines for future research that address the identified gap. In short, even though Q-Learning has many advantages, ethical issues must be bridged in order to promote responsible integration into healthcare, striking a balance between technical advancements as well as moral principles to enhance patient outcomes.