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Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment.
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making.
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories.
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings.
