摘要(英) |
Sepsis is a fatal condition of systemic inflammation caused by infection of patients. Sepsis is prevalent a prevalent bring of death in the intensive care units (ICU), costing a hospital jillion. With the development of the sepsis disease, patients’ physiological functions will be gradually damaged, and they can not maintain normal functions well. Treatment of sepsis patients will respond diversely to clinical standards. Currently, there are no generally accepted treatment guidelines for sepsis patients, and treating patients with sepsis can be very challenging. Understanding a sepsis patient’s conditions and physiological state at a specific time may explain developing a worthwhile treatment policy. In our study, we proposed a strategy capable of inferring optimal treatment for sepsis patients, using a deep reinforcement learning method to create a reference medical policy for sepsis patients, and the learned treatment policy can be used to help clinicians in intensive care units make medical decisions and improve the likelihood of patient survival. Deep reinforcement learning is widely used in the medical field, and the algorithm can perform the judgment process of human intelligence to assist humans in performing complex tasks. Our policy is slightly better than the clinician′s policy compared to the clinician′s approach and our study. Finally, our policy conforms to the policy characteristic distribution implemented by the clinician, which can be used to provide the clinician with additional support for sepsis treatment and assist physicians in medical strategies. |
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