An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging
arxiv(2024)
摘要
Deep Reinforcement Learning is an effective tool for drug dosing for chronic
condition management. However, the final protocol is generally a black box
without any justification for its prescribed doses. This paper addresses this
issue by proposing an explainable dosing protocol for warfarin using a Proximal
Policy Optimization method combined with Policy Distillation. We introduce
Action Forging as an effective tool to achieve explainability. Our focus is on
the maintenance dosing protocol. Results show that the final model is as easy
to understand and deploy as the current dosing protocols and outperforms the
baseline dosing algorithms.
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