Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electronic health records are often represented through an embedding of the data, with clustering methods used to group data of similar structure. If subgroups are known to exist within data, supervised methods may be used to influence the clusters discovered. We propose to extend deep embedded clustering to a semi-supervised deep embedded clustering algorithm to stratify subgroups through known labels in the data. In this work we apply deep semi-supervised embedded clustering to determine data-driven patient subgroups of heart failure from the electronic health records of 4,487 heart failure and control patients. We find clinically relevant clusters from an embedded space derived from heterogeneous data. The proposed algorithm can potentially find new undiagnosed subgroups of patients that have different outcomes, and, therefore, lead to improved treatments.

该研究介绍了一种基于半监督深度嵌入聚类算法的数据驱动患者亚组的确定方法，该方法利用电子健康记录中的标签信息确定病人亚组，并找到从异质数据中获得的与临床相关的簇，从而有潜力找到不同结果的未诊断患者的新亚组，从而带来改进的治疗。

深度半监督嵌入聚类（DSEC）用于对心力衰竭患者分层