Sepsis treatment is a highly challenging effort to reduce mortality in hospital intensive care units since the treatment response may vary for each patient. Tailored treatment recommendations are desired to assist doctors in making decisions efficiently and accurately. In this work, we apply a self-supervised method based on reinforcement learning (RL) for treatment recommendation on individuals. An uncertainty evaluation method is proposed to separate patient samples into two domains according to their responses to treatments and the state value of the chosen policy. Examples of two domains are then reconstructed with an auxiliary transfer learning task. A distillation method of privilege learning is tied to a variational auto-encoder framework for the transfer learning task between the low- and high-quality domains. Combined with the self-supervised way for better state and action representations, we propose a deep RL method called high-risk uncertainty (HRU) control to provide flexibility on the trade-off between the effectiveness and accuracy of ambiguous samples and to reduce the expected mortality. Experiments on the large-scale publicly available real-world dataset MIMIC-III demonstrate that our model reduces the estimated mortality rate by up to 2.3% in total, and that the estimated mortality rate in the majority of cases is reduced to 9.5%.

脓毒症治疗是降低医院重症监护病房死亡率的一项极具挑战性的工作，因为每个患者的治疗反应可能不同。需要量身定制的治疗建议来帮助医生高效准确地做出决定。在这项工作中，我们应用一种基于强化学习 (RL) 的自我监督方法来推荐个人治疗。提出了一种不确定性评估方法，根据患者对治疗的反应和所选策略的状态值，将患者样本分为两个域。然后使用辅助迁移学习任务重建两个域的示例。特权学习的蒸馏方法与低质量域和高质量域之间的迁移学习任务的变分自动编码器框架相关联。结合自我监督的方式以获得更好的状态和动作表示，我们提出了一种称为高风险不确定性（HRU）控制的深度强化学习方法，以在模糊样本的有效性和准确性之间提供灵活性，并减少预期死亡。在大规模公开可用的真实世界数据集 MIMIC-III 上的实验表明，我们的模型总共将估计死亡率降低了 2.3%，并且大多数情况下的估计死亡率降低到了 9.5%。