Sepsis is one of the deadliest diseases in North America and in spite of the vast amount of research on this topic there is still uncertainty in the outcome of sepsis treatments. This study aimed at investigating the informativeness of temporal electronic health records (EHR) in stratifying septic patients and identifying subpopulations of septic patients with similar trajectories and clinical needs. We performed hierarchical clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analyses using data from septic patients in the MIMIC III intensive care unit database. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method was utilized to map patients to a two-dimensional space. We utilized silhouette index and cluster-wise stability assessment by resampling to investigate the validity of the clusters. The hierarchical clustering with Euclidean metric identified twelve clinically recognizable subgroups that demonstrated different characteristics in spite of sharing common conditions. Our results demonstrated that data-driven approaches can help in customizing care platforms for septic patients by identifying similar clinically relevant groups.

败血症是北美最致命的疾病之一，尽管对此主题进行了大量研究，但脓毒症治疗的结果仍存在不确定性。这项研究旨在调查时间电子健康记录（EHR）在脓毒症患者分层中的信息性，并确定具有相似轨迹和临床需求的脓毒症患者亚群。我们使用来自MIMIC III重症监护病房数据库中败血症患者的数据，进行了分层聚类和基于密度的应用噪声空间聚类（DBSCAN）分析。使用t分布随机邻居嵌入（t-SNE）方法将患者映射到二维空间。通过重新采样，我们利用了轮廓指数和聚类明智性评估来研究聚类的有效性。使用欧几里得度量的分层聚类确定了十二个临床上可识别的亚组，这些亚组尽管具有共同的条件，但仍表现出不同的特征。我们的结果表明，数据驱动的方法可以通过识别相似的临床相关人群来帮助定制败血症患者的护理平台。