Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment

• URL: http://arxiv.org/abs/2101.03309v1
• Date: Sat, 9 Jan 2021 07:15:33 GMT
• Title: Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
• Abstract summary: We develop a novel framework to compress continuous trajectories into a few, interpretable decision points. We apply our approach to create recommendations from a cohort of hypotensive patients dataset.
• Score: 24.010934215216142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many batch RL health applications first discretize time into fixed intervals. However, this discretization both loses resolution and forces a policy computation at each (potentially fine) interval. In this work, we develop a novel framework to compress continuous trajectories into a few, interpretable decision points --places where the batch data support multiple alternatives. We apply our approach to create recommendations from a cohort of hypotensive patients dataset. Our reduced state space results in faster planning and allows easy inspection by a clinical expert.

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