Figure 3: Reinforcement learning framework and clinical application.

An algorithm interacts with its environment (consisting of data from electronic health records or datasets) to learn states (representing disease or patient acuity), actions that lead to new states, probabilities of transitioning between states, and associations between state transitions and an ultimate goal, such as survival or discharge to home in good health. The algorithm then identifies actions that are most likely to achieve the ultimate goal. This process can occur within a Markov Decision Process framework and apply to a patient presenting with bowel obstruction, estimating the clinical utility of observation and operative exploration in response to evolving clinical conditions.