Heterogeneous Similarity Graph Neural Network on Electronic Health
Records
- URL: http://arxiv.org/abs/2101.06800v1
- Date: Sun, 17 Jan 2021 23:14:29 GMT
- Title: Heterogeneous Similarity Graph Neural Network on Electronic Health
Records
- Authors: Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu
- Abstract summary: We propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN.
Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN.
The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make a prediction.
- Score: 74.66674469510251
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mining Electronic Health Records (EHRs) becomes a promising topic because of
the rich information they contain. By learning from EHRs, machine learning
models can be built to help human experts to make medical decisions and thus
improve healthcare quality. Recently, many models based on sequential or graph
models are proposed to achieve this goal. EHRs contain multiple entities and
relations and can be viewed as a heterogeneous graph. However, previous studies
ignore the heterogeneity in EHRs. On the other hand, current heterogeneous
graph neural networks cannot be simply used on an EHR graph because of the
existence of hub nodes in it. To address this issue, we propose Heterogeneous
Similarity Graph Neural Network (HSGNN) analyze EHRs with a novel heterogeneous
GNN. Our framework consists of two parts: one is a preprocessing method and the
other is an end-to-end GNN. The preprocessing method normalizes edges and
splits the EHR graph into multiple homogeneous graphs while each homogeneous
graph contains partial information of the original EHR graph. The GNN takes all
homogeneous graphs as input and fuses all of them into one graph to make a
prediction. Experimental results show that HSGNN outperforms other baselines in
the diagnosis prediction task.
Related papers
- Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN.
We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
arXiv Detail & Related papers (2023-05-18T07:27:18Z) - Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering [15.764819403555512]
It is impossible to first identify a graph as homophilic or heterophilic before a suitable GNN model can be found.
We propose a novel graph clustering method, which contains three key components: graph reconstruction, a mixed filter, and dual graph clustering network.
Our method dominates others on heterophilic graphs.
arXiv Detail & Related papers (2023-05-03T01:49:01Z) - GCNH: A Simple Method For Representation Learning On Heterophilous
Graphs [4.051099980410583]
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs.
Recent works have proposed extensions to standard GNN architectures to improve performance on heterophilous graphs.
We propose GCN for Heterophily (GCNH), a simple yet effective GNN architecture applicable to both heterophilous and homophilous scenarios.
arXiv Detail & Related papers (2023-04-21T11:26:24Z) - Relation Embedding based Graph Neural Networks for Handling
Heterogeneous Graph [58.99478502486377]
We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs.
Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
arXiv Detail & Related papers (2022-09-23T05:24:18Z) - Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach [43.41163711340362]
We propose a method named Deep Heterophily Graph Rewiring (DHGR) to rewire graphs by adding homophilic edges and pruning heterophilic edges.
To the best of our knowledge, it is the first work studying graph rewiring for heterophily graphs.
arXiv Detail & Related papers (2022-09-17T06:55:21Z) - Heterogeneous Graph Neural Networks using Self-supervised Reciprocally
Contrastive Learning [102.9138736545956]
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.
We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies.
In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
arXiv Detail & Related papers (2022-04-30T12:57:02Z) - Graph Neural Networks for Graphs with Heterophily: A Survey [98.45621222357397]
We provide a comprehensive review of graph neural networks (GNNs) for heterophilic graphs.
Specifically, we propose a systematic taxonomy that essentially governs existing heterophilic GNN models.
We discuss the correlation between graph heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs.
arXiv Detail & Related papers (2022-02-14T23:07:47Z) - Meta-path Free Semi-supervised Learning for Heterogeneous Networks [16.641434334366227]
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification.
In this paper, we propose simple and effective graph neural networks for heterogeneous graph, excluding the use of meta-paths.
arXiv Detail & Related papers (2020-10-18T06:01:58Z) - Heterogeneous Graph Transformer [49.675064816860505]
Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs.
To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT.
To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm---HGSampling---for efficient and scalable training.
arXiv Detail & Related papers (2020-03-03T04:49:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.