Modelling Information Perceiving Within Clinical Decision Support Using Inverse Reinforcement Learning

Decision support systems in the medical domain is budding field that aims to improve healthcare and overall recovery for patients. While treatment remains specific to individual symptoms, the diagnosis of patients is fairly general. Interpreting the diagnosis and assigning the appropriate care treatment is a crucial part undertaken by medical professionals, however, in critical scenarios, having access to recommendations from a clinical decision support system may prove life-saving. We present a real-world application of inverse reinforcement learning (IRL) to assess the implicit cognitive state of doctors when evaluating decision support data on a patient’s risk of acquiring Type 2 Diabetes mellitus (T2DM). We show the underlying process of modelling a Markov Decision Process (MDP) using real-world clinical data and experiment with various policies extracted from sampled trajectories. The results provide insights into the approach to modelling real-world data into interpretable solutions via IRL.