School of Public Health and Health Systems, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada.
School of Public Health and Health Systems, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada; Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada.
Comput Biol Med. 2021 Mar;130:104182. doi: 10.1016/j.compbiomed.2020.104182. Epub 2020 Dec 19.
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，尽管针对这一主题进行了大量研究，但败血症治疗的结果仍存在不确定性。本研究旨在调查时间电子健康记录（EHR）在分层败血症患者和识别具有相似轨迹和临床需求的败血症患者亚群方面的信息性。我们使用来自 MIMIC III 重症监护病房数据库的败血症患者的数据进行层次聚类和基于密度的空间聚类应用程序噪声（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。我们利用轮廓指数和通过重新采样进行的聚类稳定性评估来研究聚类的有效性。基于欧几里得度量的层次聚类确定了十二个临床上可识别的亚组，尽管它们具有共同的条件，但表现出不同的特征。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组，帮助为败血症患者定制护理平台。
