Reinforcement learning for treatment decision-making in sepsis: a scoping review.
Most reinforcement learning studies for sepsis treatment use the Medical Information Mart for Intensive Care database.
Reinforcement learning for treatment decision-making in sepsis: a scoping review.
This scoping review summarizes the progress of reinforcement learning (reinforcement learning) in clinical decision-making for sepsis at the intersection of medicine and artificial intelligence (artificial intelligence).
All 72 included studies were retrospective, with the majority using the Medical Information Mart for Intensive Care (Medical Information Mart for Intensive Care) database (58 studies [80.6%]), and relatively few employing private datasets (10 studies [13.9%]).
Study designs varied widely, especially in state representation, action space, reward definition, and choice of algorithms.
Most research focused on vasopressor and intravenous fluid management, while fewer studies addressed antibiotics, corticosteroids, mechanical ventilation, heparin, or vasopressin.
Ultimately, advancing interpretability, generalizability, and safety will be critical to effectively integrating reinforcement learning into routine clinical practice.