We propose a novel approach for interpreting deep embeddings in the context
of patient clustering. We evaluate our approach on a dataset of participants
with type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful
insights into disease progression patterns.

本篇研究提出了一种新的深度嵌入解释方法用于患者聚类，以英国生物银行中的 2 型糖尿病参与者数据为评估标准，展示了对疾病进展模式的临床意义上有意义的见解。

深度嵌入解释疾病进展聚类

Interpreting deep embeddings for disease progression clustering

Driven by the multi-level structure of human intracranial
electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a
new variant of a hierarchical Dirichlet Process---the multi-level clustering
hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters
datasets on multiple levels. Our seizure dataset contains brain activity
recorded in typically more than a hundred individual channels for each seizure
of each patient. The MLC-HDP model clusters over channels-types, seizure-types,
and patient-types simultaneously. We describe this model and its implementation
in detail. We also present the results of a simulation study comparing the
MLC-HDP to a similar model, the Nested Dirichlet Process and finally
demonstrate the MLC-HDP's use in modeling seizures across multiple patients. We
find the MLC-HDP's clustering to be comparable to independent human physician
clusterings. To our knowledge, the MLC-HDP model is the first in the epilepsy
literature capable of clustering seizures within and between patients.

本文介绍了一种新的多层次聚类方法（多层聚类 Dirichlet 过程），用于对癫痫发作进行分析，该方法能够在多个层面上同时对数据集进行聚类，包括通道、发作类型和患者类型等，并通过模拟研究得出了该模型的可行性。