# Heterogeneous Similarity Graph Neural Network on Electronic Health Records

arXiv: https://arxiv.org/abs/2101.06800 · HTML: https://mlsanity.com/paper/2101.06800/

## 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 (most cited first, 29 of 29)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [PairNorm: Tackling Oversmoothing in GNNs](https://mlsanity.com/paper/1909.12223/) (2019)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare](https://mlsanity.com/paper/1810.09593/) (2018)
- [Multi-layer Representation Learning for Medical Concepts](https://mlsanity.com/paper/1602.05568/) (2016)
- [MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records](https://mlsanity.com/paper/1905.03218/) (2019)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/1906.04580/) (2019)
- [Basket Recommendation with Multi-Intent Translation Graph Neural Network](https://mlsanity.com/paper/2010.11419/) (2020)
- [Medical Concept Embedding with Time-Aware Attention](https://mlsanity.com/paper/1806.02873/) (2018)
- [HeteroMed: Heterogeneous Information Network for Medical Diagnosis](https://mlsanity.com/paper/1804.08052/) (2018)
- [Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks](https://mlsanity.com/paper/1701.06675/) (2017)
- [PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks](https://mlsanity.com/paper/1706.01177/) (2017)

## Built on by (most cited first, 30 of 30)

- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs](https://mlsanity.com/paper/2305.09858/) (2023)
- [Few-shot Node Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2301.02708/) (2023)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction](https://mlsanity.com/paper/2304.11794/) (2023)
- [A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1](https://mlsanity.com/paper/2312.17506/) (2023)
- [TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://mlsanity.com/paper/2406.10061/) (2024)
- [MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression](https://mlsanity.com/paper/2305.11742/) (2023)
- [Valid Conformal Prediction for Dynamic GNNs](https://mlsanity.com/paper/2405.19230/) (2024)
- [Multi-task Heterogeneous Graph Learning on Electronic Health Records](https://mlsanity.com/paper/2408.07569/) (2024)
- [Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs](https://mlsanity.com/paper/2502.13555/) (2025)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Graph AI in Medicine](https://mlsanity.com/paper/2310.13767/) (2023)
- [DyExplainer: Explainable Dynamic Graph Neural Networks](https://mlsanity.com/paper/2310.16375/) (2023)
- [A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder](https://mlsanity.com/paper/2308.11819/) (2023)
- [Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction](https://mlsanity.com/paper/2308.12575/) (2023)
- [Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks](https://mlsanity.com/paper/2312.02387/) (2023)
- [DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning](https://mlsanity.com/paper/2603.10180/) (2026)
- [A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks](https://mlsanity.com/paper/2301.11608/) (2023)
- [Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent](https://mlsanity.com/paper/2311.01205/) (2023)
- [A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes](https://mlsanity.com/paper/2401.03700/) (2024)
- [GNN's Uncertainty Quantification using Self-Distillation](https://mlsanity.com/paper/2506.20046/) (2025)
- [Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction](https://mlsanity.com/paper/2508.17554/) (2025)
- [APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification](https://mlsanity.com/paper/2512.18473/) (2025)
- [MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records](https://mlsanity.com/paper/2608.06430/) (2026)
- [Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records](https://mlsanity.com/paper/2610.20502/) (2026)

## 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.