Optimizing active surveillance for prostate cancer using partially observable Markov decision processes.
Weiyu LiBrian T. DentonTodd M. MorganPublished in: Eur. J. Oper. Res. (2023)
Keyphrases
- prostate cancer
- partially observable markov decision processes
- finite state
- belief state
- computer aided
- belief space
- reinforcement learning
- image guided
- dynamical systems
- dynamic programming
- decision problems
- optimal policy
- mr images
- partially observable markov
- radiation therapy
- state space
- partially observable stochastic games
- multi agent
- planning problems
- partially observable
- markov decision processes
- medical image analysis
- magnetic resonance spectroscopy
- machine learning
- domain independent
- markov chain
- prostate segmentation
- learning algorithm
- approximate solutions
- knowledge base
- multi agent systems
- dynamic environments
- utility function