The Intensive Care Unit (ICU) is one of the most important parts of a
hospital, which admits critically ill patients and provides continuous
monitoring and treatment. Various patient outcome prediction methods have been
attempted to assist healthcare professionals in clinical decision-making.
Existing methods focus on measuring the similarity between patients using deep
neural networks to capture the hidden feature structures. However, the
higher-order relationships are ignored, such as patient characteristics (e.g.,
diagnosis codes) and their causal effects on downstream clinical predictions.
In this paper, we propose a novel Hypergraph Convolutional Network that
allows the representation of non-pairwise relationships among diagnosis codes
in a hypergraph to capture the hidden feature structures so that fine-grained
patient similarity can be calculated for personalized mortality risk
prediction. Evaluation using a publicly available eICU Collaborative Research
Database indicates that our method achieves superior performance over the
state-of-the-art models on mortality risk prediction. Moreover, the results of
several case studies demonstrated the effectiveness of constructing graph
networks in providing good transparency and robustness in decision-making.

我们提出了一种新颖的超图卷积网络，可以捕捉隐藏的特征结构，计算个性化的死亡风险预测，评估表明我们的方法在死亡风险预测上优于现有模型，并且几个案例研究的结果证明了构建图网络在决策中提供良好的透明性和稳健性。

超图卷积网络用于精细化 ICU 病人相似性分析和风险预测

Hypergraph Convolutional Networks for Fine-grained ICU Patient  Similarity Analysis and Risk Prediction

A bipartite network is a graph structure where nodes are from two distinct
domains and only inter-domain interactions exist as edges. A large number of
network embedding methods exist to learn vectorial node representations from
general graphs with both homogeneous and heterogeneous node and edge types,
including some that can specifically model the distinct properties of bipartite
networks. However, these methods are inadequate to model multiplex bipartite
networks (e.g., in e-commerce), that have multiple types of interactions (e.g.,
click, inquiry, and buy) and node attributes. Most real-world multiplex
bipartite networks are also sparse and have imbalanced node distributions that
are challenging to model. In this paper, we develop an unsupervised Dual
HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the
multiplex bipartite network into two sets of homogeneous hypergraphs and uses
spectral hypergraph convolutional operators, along with intra- and
inter-message passing strategies to promote information exchange within and
across domains, to learn effective node embedding. We benchmark DualHGCN using
four real-world datasets on link prediction and node classification tasks. Our
extensive experiments demonstrate that DualHGCN significantly outperforms
state-of-the-art methods, and is robust to varying sparsity levels and
imbalanced node distributions.

本文提出了一种无监督的 Dual HyperGraph Convolutional Network (DualHGCN) 模型，通过将多重二分网络转化为两组同质超图，并使用谱超图卷积算子以及内部和跨域间的信息传递策略来学习有效的节点嵌入，对四个真实数据集进行基准测试，展示了 DualHGCN 显著优于现有最先进方法，并且对不同的稀疏水平和不平衡节点分布具有鲁棒性。

双超图卷积神经网络应用于多路二分网络嵌入

Multiplex Bipartite Network Embedding using Dual Hypergraph  Convolutional Networks

Social relations are often used to improve recommendation quality when
user-item interaction data is sparse in recommender systems. Most existing
social recommendation models exploit pairwise relations to mine potential user
preferences. However, real-life interactions among users are very complicated
and user relations can be high-order. Hypergraph provides a natural way to
model complex high-order relations, while its potentials for improving social
recommendation are under-explored. In this paper, we fill this gap and propose
a multi-channel hypergraph convolutional network to enhance social
recommendation by leveraging high-order user relations. Technically, each
channel in the network encodes a hypergraph that depicts a common high-order
user relation pattern via hypergraph convolution. By aggregating the embeddings
learned through multiple channels, we obtain comprehensive user representations
to generate recommendation results. However, the aggregation operation might
also obscure the inherent characteristics of different types of high-order
connectivity information. To compensate for the aggregating loss, we
innovatively integrate self-supervised learning into the training of the
hypergraph convolutional network to regain the connectivity information with
hierarchical mutual information maximization. The experimental results on
multiple real-world datasets show that the proposed model outperforms the SOTA
methods, and the ablation study verifies the effectiveness of the multi-channel
setting and the self-supervised task. The implementation of our model is
available via this https URL

本文提出了多通道超图卷积网络模型，通过利用高阶用户关系来提高社交推荐系统的推荐质量，结合自我监督学习的方法以弥补聚合信息的损失。在多个真实数据集上的实验结果表明，该模型优于现有的其他方法。