A DQN trained on random-forest-simulated bladder cancer trajectories reports high internal rewards, but the evaluation is circular and the reported numbers are not supported by the data.
Prediction of Cancer Treatment Effectiveness and Patient Outcomes using Machine Learning Classification Approaches - A Review
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Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support Framework
A DQN trained on random-forest-simulated bladder cancer trajectories reports high internal rewards, but the evaluation is circular and the reported numbers are not supported by the data.