Personalised medicine in critical care using Bayesian reinforcement learning

Patients with similar conditions in the intensive care unit (ICU) may have different reactions for a given treatment. An effective personalised medicine can help save patient lives. The availability of recorded ICU data provides a huge potential to train and develop the systems. However, there is no ground truth of best treatments. This makes existing supervised learning based methods are not appropriate. In this paper, we proposed clustering based Bayesian reinforcement learning. Firstly, we transformed the multivariate time series patient record into a real-time Patient Sequence Model (PSM). After that, we computed the likelihood probability of treatments effect for all patients and cluster them based on that. Finally, we computed Bayesian reinforcement learning to derive personalised policies. We tested our proposed method using 11,791 ICU patients records from MIMIC-III database. Results show that we are able to cluster patient based on their treatment effects. In addition, our method also provides better explainability and time-critical recommendation that are very important in a real ICU setting