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Heterogeneous Similarity Graph Neural Network on Electronic Health Records

arXiv 2101.06800 ↗First posted 17 Jan 2021cs.LGcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 17 Jan 2021.

Builds on

29 of 41 references.

References · 29

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
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  6. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  7. 2019Heterogeneous Graph Attention Networkcited by 734
  8. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  9. 2016RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanismcited by 349
  10. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  11. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
  12. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
17 more
  1. 2015Doctor AI: Predicting Clinical Events via Recurrent Neural Networkscited by 208
  2. 2016GRAM: Graph-based Attention Model for Healthcare Representation Learningcited by 156
  3. 2017Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networkscited by 116
  4. 2015A Survey of Heterogeneous Information Network Analysiscited by 110
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  6. 2019Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformercited by 88
  7. 2018GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combinationcited by 83
  8. 2018MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcarecited by 72
  9. 2016Multi-layer Representation Learning for Medical Conceptscited by 69
  10. 2019MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Recordscited by 33
  11. 2021Dynamic Graph Collaborative Filteringcited by 33
  12. 2019Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networkscited by 23
  13. 2020Basket Recommendation with Multi-Intent Translation Graph Neural Networkcited by 16
  14. 2018Medical Concept Embedding with Time-Aware Attentioncited by 11
  15. 2018HeteroMed: Heterogeneous Information Network for Medical Diagnosiscited by 6
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 13; 2024: 6; 2025: 4; 2026: 3.

30 papers.

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  1. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  2. 2022Graph Neural Networks in IoT: A Surveycited by 28
  3. 2023Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMscited by 24
  4. 2023Few-shot Node Classification with Extremely Weak Supervisioncited by 14
  5. 2024Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directionscited by 13
  6. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  7. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  8. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  9. 2023FineEHR: Refine Clinical Note Representations to Improve Mortality Predictioncited by 9
  10. 2023A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1cited by 7
  11. 2024TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Datacited by 7
  12. 2023MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regressioncited by 6
18 more
  1. 2024Valid Conformal Prediction for Dynamic GNNscited by 6
  2. 2024Multi-task Heterogeneous Graph Learning on Electronic Health Recordscited by 5
  3. 2025Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphscited by 5
  4. 2023Time-aware Hyperbolic Graph Attention Network for Session-based Recommendationcited by 3
  5. 2023Graph AI in Medicinecited by 3
  6. 2023DyExplainer: Explainable Dynamic Graph Neural Networkscited by 2
  7. 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfoundercited by 1
  8. 2023Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Predictioncited by 1
  9. 2023Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networkscited by 1
  10. 2026DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learningcited by 1
  11. 2023A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networkscited by 0
  12. 2023Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferentcited by 0
  13. 2024A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomescited by 0
  14. 2025GNN's Uncertainty Quantification using Self-Distillationcited by 0
  15. 2025Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Predictioncited by 0
  16. 2025APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classificationcited by 0
  17. 2026MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Recordscited by 0
  18. 2026Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Recordscited by 0