论文标题
最佳败血症患者使用人工智能治疗
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
论文作者
论文摘要
败血症是重症监护病房（ICU）中死亡的主要原因之一。治疗败血症的策略涉及静脉内（IV）液和抗生素的给药。由于患者生理的复杂性，确定最佳静脉输液量是一个具有挑战性的问题。在这项研究中，我们开发了一个数据驱动的优化解决方案，该解决方案可为个别患者提供最佳的静脉输液量。提出的方法通过控制规定的静脉输液数量并利用人工人工智能来最大程度地减少严重结果的可能性。我们证明了模型对从模拟物III数据集提取的1122例ICU患者的表现。结果表明，平均而言，我们的模型可以将死亡率降低22％。这项研究有可能帮助医生综合最佳，特定于患者的治疗策略。
Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV) fluids and administration of antibiotics. Determining the optimal quantity of IV fluids is a challenging problem due to the complexity of a patient's physiology. In this study, we develop a data-driven optimization solution that derives the optimal quantity of IV fluids for individual patients. The proposed method minimizes the probability of severe outcomes by controlling the prescribed quantity of IV fluids and utilizes human-in-the-loop artificial intelligence. We demonstrate the performance of our model on 1122 ICU patients with sepsis diagnosis extracted from the MIMIC-III dataset. The results show that, on average, our model can reduce mortality by 22%. This study has the potential to help physicians synthesize optimal, patient-specific treatment strategies.
