Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computer aided fuzzy medical diagnosis
Information Sciences: an International Journal - Special issue: Medical expert systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
HepatoConsult: a knowledge-based second opinion and documentation system
Artificial Intelligence in Medicine
Stochastic optimal control for small noise intensities: the discrete-time case
WSEAS Transactions on Mathematics
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In this study we develop a decision support architecture that evaluates pathology findings for defining levels of chronic hepatitis B, and models patients' clinical stages for assisting treatment decisions. It is a learning system that generates a feedback to pathological diagnostic as well as to the clinical decision making by using reinforcement learning techniques. The system receives reinforcement from the patient as a consequence of undertaken actions during a treatment plan. This received information leads system to learn from experiences such as the patient's response to the treatment and evaluations of related parties (pathologist and clinicians).