The growing availability of well-organized Electronic Health Records (EHR)
data has enabled the development of various machine learning models towards
disease risk prediction. However, existing risk prediction methods overlook the
heterogeneity of complex diseases, failing to model the potential disease
subtypes regarding their corresponding patient visits and clinical concept
subgroups. In this work, we introduce TACCO, a novel framework that jointly
discovers clusters of clinical concepts and patient visits based on a
hypergraph modeling of EHR data. Specifically, we develop a novel
self-supervised co-clustering framework that can be guided by the risk
prediction task of specific diseases. Furthermore, we enhance the hypergraph
model of EHR data with textual embeddings and enforce the alignment between the
clusters of clinical concepts and patient visits through a contrastive
objective. Comprehensive experiments conducted on the public MIMIC-III dataset
and Emory internal CRADLE dataset over the downstream clinical tasks of
phenotype classification and cardiovascular risk prediction demonstrate an
average 31.25% performance improvement compared to traditional ML baselines and
a 5.26% improvement on top of the vanilla hypergraph model without our
co-clustering mechanism. In-depth model analysis, clustering results analysis,
and clinical case studies further validate the improved utilities and
insightful interpretations delivered by TACCO. Code is available at
this https URL

基于超图建模的 TACCO 方法，通过自监督的共聚类框架，发现临床概念和患者访问的簇类，进一步加强了电子医疗记录数据的超图模型，并通过对比目标实现了临床概念和患者访问的簇类的对齐，实验证明了 TACCO 提供了改进的效用和深入的解释。

TACCO: 基于电子病历数据的疾病亚型划分的临床概念和患者访问的任务引导合并聚类

TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits  for Disease Subtyping based on EHR Data

Session-based recommendation (SBR) focuses on next-item prediction at a
certain time point. As user profiles are generally not available in this
scenario, capturing the user intent lying in the item transitions plays a
pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the
item transitions as pairwise relations, which neglect the complex high-order
information among items. Hypergraph provides a natural way to capture
beyond-pairwise relations, while its potential for SBR has remained unexplored.
In this paper, we fill this gap by modeling session-based data as a hypergraph
and then propose a hypergraph convolutional network to improve SBR. Moreover,
to enhance hypergraph modeling, we devise another graph convolutional network
which is based on the line graph of the hypergraph and then integrate
self-supervised learning into the training of the networks by maximizing mutual
information between the session representations learned via the two networks,
serving as an auxiliary task to improve the recommendation task. Since the two
types of networks both are based on hypergraph, which can be seen as two
channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel
Hypergraph Convolutional Networks). Extensive experiments on three benchmark
datasets demonstrate the superiority of our model over the SOTA methods, and
the results validate the effectiveness of hypergraph modeling and
self-supervised task. The implementation of our model is available at
this https URL

本文提出了一种基于超图的卷积网络模型 DHCN，通过建模会话数据为超图并将自监督学习整合到网络的训练中，从而提高了会话推荐任务的准确性。在三个基准数据集上进行广泛实验，证明了该模型在普遍情况下胜过基线模型。