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.

翻译：脓毒症是一种因感染而引发的综合征，以严重器官功能障碍为特征，是全球重症监护病房（ICU）患者死亡的主要原因之一。早期应用抗生素可减少相关并发症，因此预测脓毒症发作的能力对患者的生存与康复至关重要。目前部署于医疗基础设施中的传统机器学习算法表现不佳，难以有效实现脓毒症早期预警。近年来，深度学习方法被提出用于脓毒症预测，但部分方法未能准确捕捉发作时间（例如：将患者的完整就诊过程分类为是否发展为脓毒症），另一些方法则因不切实际而难以部署于医疗机构（例如：基于固定时间窗口构建训练样本，需预先知晓发作时间）。为此，本文首先提出一种新颖且现实的预测框架，利用夜间采集的数据（夜间因交叉覆盖导致患者-医护人员比例升高，对每位患者的观察有限），于每日清晨预测未来24小时内脓毒症是否发作。然而，随着预测频率提升至每日一次，负样本数量增加而正样本数量保持不变，导致严重的类别不平衡问题，使得机器学习模型难以识别罕见的脓毒症病例。为解决此问题，我们提出为每位患者进行夜间状态表征学习（NPRL）。理论上证明NPRL可有效缓解罕见事件问题。基于某一级创伤中心数据的实证研究进一步验证了本方法的有效性。