Individualized sepsis treatment using reinforcement learning

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reinforcement Learning in Healthcare: A Survey (2019, background): “techniques to the problem of deducing optimal treatment policies for patients with sepsis [228].”
Towards Safe Policy Improvement for Non-Stationary MDPs (2020, unknown): “For example, researchers have proposed the use of reinforcement learning algorithms for several medical support systems, ranging from diabetes management [5], to epilepsy [46], to sepsis treatment [50].”
Calibrated Model-Based Deep Reinforcement Learning (2019, methodology): “Probabilistic methods have been used to improve the safety, interpretability, and performance of decision-making agents in various domains, including medicine (Saria, 2018), robotics (Chua et al., 2018; Buckman et al., 2018), and operations research (Van Roy et al., 1997).”