Sepsis is a syndrome that develops in response to the presence of infection. It is characterized by severe organ dysfunction and is one of the leading causes of mortality in Intensive Care Units (ICUs) worldwide. These complications can be reduced through early application of antibiotics, hence the ability to anticipate the onset of sepsis early is crucial to the survival and well-being of patients. Current machine learning algorithms deployed inside medical infrastructures have demonstrated poor performance and are insufficient for anticipating sepsis onset early. In recent years, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e.g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic to be deployed into medical facilities (e.g., creating training instances using a fixed time to onset where the time of onset needs to be known apriori). Therefore, in this paper, we first propose a novel but realistic prediction framework that predicts each morning whether sepsis onset will occur within the next 24 hours using data collected at night, when patient-provider ratios are higher due to cross-coverage resulting in limited observation to each patient. However, as we increase the prediction rate into daily, the number of negative instances will increase while that of positive ones remain the same. Thereafter, we have a severe class imbalance problem, making a machine learning model hard to capture rare sepsis cases. To address this problem, we propose to do nightly profile representation learning (NPRL) for each patient. We prove that NPRL can theoretically alleviate the rare event problem. Our empirical study using data from a level-1 trauma center further demonstrates the effectiveness of our proposal.

本文提出了一种实现在医学设施内部署的早期预测严重感染的方法，使用机器学习与深度学习方法，研究人员还提出了夜间患者个体风险特征向量的学习方法，通过实验验证了该方法的有效性。

NPRL: ICU 创伤患者早期脓毒症发病预测之夜间档案表征学习