  Sepsis is a leading cause of death in the ICU. It is a disease requiring
complex interventions in a short period of time, but its optimal treatment
strategy remains uncertain. Evidence suggests that the practices of currently
used treatment strategies are problematic and may cause harm to patients. To
address this decision problem, we propose a new medical decision model based on
historical data to help clinicians recommend the best reference option for
real-time treatment. Our model combines offline reinforcement learning and deep
reinforcement learning to solve the problem of traditional reinforcement
learning in the medical field due to the inability to interact with the
environment, while enabling our model to make decisions in a continuous
state-action space. We demonstrate that, on average, the treatments recommended
by the model are more valuable and reliable than those recommended by
clinicians. In a large validation dataset, we find out that the patients whose
actual doses from clinicians matched the decisions made by AI has the lowest
mortality rates. Our model provides personalized and clinically interpretable
treatment decisions for sepsis to improve patient care.
