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.

翻译：深度强化学习是慢性病管理药物剂量调整的有效工具。然而，最终方案通常是一个黑箱模型，无法解释其推荐剂量的依据。为解决这一问题，本文提出了一种结合近端策略优化方法与策略蒸馏的可解释华法林剂量方案。我们引入"动作伪造"作为实现可解释性的有效工具，重点关注维持剂量方案的研究。结果表明，该最终模型既易于理解部署，又优于现有基线给药算法，其可操作性堪比当前临床剂量方案。