The process of manually searching for relevant instances in, and extracting
information from, clinical databases underpin a multitude of clinical tasks.
Such tasks include disease diagnosis, clinical trial recruitment, and
continuing medical education. This manual search-and-extract process, however,
has been hampered by the growth of large-scale clinical databases and the
increased prevalence of unlabelled instances. To address this challenge, we
propose a supervised contrastive learning framework, CROCS, where
representations of cardiac signals associated with a set of patient-specific
attributes (e.g., disease class, sex, age) are attracted to learnable
embeddings entitled clinical prototypes. We exploit such prototypes for both
the clustering and retrieval of unlabelled cardiac signals based on multiple
patient attributes. We show that CROCS outperforms the state-of-the-art method,
DTC, when clustering and also retrieves relevant cardiac signals from a large
database. We also show that clinical prototypes adopt a semantically meaningful
arrangement based on patient attributes and thus confer a high degree of
interpretability.

该研究提出一种监督式对比学习框架 CROCS，它可以将与病人特定属性（如疾 病类别、性别、年龄）相关联的心脏信号的表示学习到可以学习的临床原型嵌入 中。通过使用这些原型，可以根据多个病人属性聚类和检索无标签的心脏信号。该 框架在聚类和从大型数据库检索相关心脏信号方面优于现有的方法，并且临床原型 根据病人属性采用语义相关性布局，从而提供了高度可解释性。