Reinforcement learning
Reinforcement learning has recently delivered breakthroughs in the biomedical field. This strategy is characterized by an iterative process that aims to take actions that deliver maximum reward based on a defined objective function. This is comparable to nature, where animals have learned to interpret signs such as hunger as negative, whereas satiety after food intake is seen as positive reinforcement. When animals learn how to behave to gain optimal positive reinforcement, they show reinforcement learning. Applications within medicine have indicated its potential. For example, an “AI Clinician” algorithm has been developed to improve the treatment of sepsis by suggesting the personalized treatment of intravenous fluids and vasopressors. While still requiring prospective validation, an independent validation cohort was used to assess this algorithm, showing that mortality was lowest when clinicians’ treatment policy was close to AI recommended policy and higher when deviating from it.