CROCS：基于患者疾病类别、性别和年龄的心脏信号聚类和检索
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
摘要：
在临床数据库中手动搜索相关实例并从临床数据库中提取信息的过程支撑了大量临床任务。这些任务包括疾病诊断、临床试验招募和继续医学教育。然而，这种人工搜索和提取过程受到了大规模临床数据库的增长和未标记实例日益普遍的阻碍。为了应对这一挑战，我们提出了一个监督对比学习框架，CROCS，其中心脏信号的表示与一组患者特定属性（例如，疾病类别、性别、年龄）被称为临床原型的可学习嵌入所吸引。我们利用这样的原型的聚类和检索的未标记的心脏信号的基础上，多个病人的属性。我们表明，CROCS优于国家的最先进的方法，DTC，聚类时，也检索相关的心脏信号从一个大型数据库。我们还表明，临床原型采用基于患者属性的语义上有意义的安排，从而赋予高度的可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
