Sepsis is a leading cause of mortality in intensive care units and costs
hospitals billions annually. Treating a septic patient is highly challenging,
because individual patients respond very differently to medical interventions
and there is no universally agreed-upon treatment for sepsis. In this work, we
propose an approach to deduce treatment policies for septic patients by using
continuous state-space models and deep reinforcement learning. Our model learns
clinically interpretable treatment policies, similar in important aspects to
the treatment policies of physicians. The learned policies could be used to aid
intensive care clinicians in medical decision making and improve the likelihood
of patient survival.

该研究提出了一种使用连续状态空间模型和深度强化学习推断感染性休克患者治疗方针的方法，模型可以学习临床可解释的治疗方针，为深度减轻和改善患者生存率提供辅助。

深度强化学习用于脓毒症治疗

Deep Reinforcement Learning for Sepsis Treatment

Sepsis is a leading cause of mortality in intensive care units (ICUs) and
costs hospitals billions annually. Treating a septic patient is highly
challenging, because individual patients respond very differently to medical
interventions and there is no universally agreed-upon treatment for sepsis.
Understanding more about a patient's physiological state at a given time could
hold the key to effective treatment policies. In this work, we propose a new
approach to deduce optimal treatment policies for septic patients by using
continuous state-space models and deep reinforcement learning. Learning
treatment policies over continuous spaces is important, because we retain more
of the patient's physiological information. Our model is able to learn
clinically interpretable treatment policies, similar in important aspects to
the treatment policies of physicians. Evaluating our algorithm on past ICU
patient data, we find that our model could reduce patient mortality in the
hospital by up to 3.6% over observed clinical policies, from a baseline
mortality of 13.7%. The learned treatment policies could be used to aid
intensive care clinicians in medical decision making and improve the likelihood
of patient survival.

使用连续状态空间模型和深度强化学习来推断感染症患者的最佳治疗方案，能够减少患者在医院内的死亡率。