Optimizing Personalized Treatment Decisions Using Deep Q-Networks in Reinforcement Learning
Arpita Nayak, Ipseeta Satpathy, Vishal Kumar Jain · Advances in computational intelligence and robotics book series · 2025
Healthcare treatments have found new paths through personalized strategy development made possible by Deep Q-Networks (DQNs) working together with computational intelligence and data-driven adaptive choices. Medical diagnosis receives support from DQNs through their analysis of extensive patient data combinations including reports together with test results and imaging data to establish diagnostic trends which boost diagnostic accuracy while functioning. The prescription process of drugs becomes more effective using DQNs because they deploy simulation techniques to identify the best drug treatment combinations by processing both genetic data and patient-specific medical status. DQNs generate adjustable treatment strategies by processing live patient feedback that optimizes the management of long-term health problems especially when treating diabetes or cancer. The full capabilities of this technology require resolving present privacy issues and interpretability barriers and workflow implementation requirements to achieve the potential outcomes.