mlsanityData to 7 Oct 2026

HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks

arXiv 2403.18128 ↗First posted 26 Mar 2024cs.LG cs.CYcited by 3

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Abstract (arXiv)

While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications. Our code is available at https://github.com/healthylaife/HealthGAT

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

15 of 41 references.

References · 15

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  6. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  7. 2015LINE: Large-scale Information Network Embeddingcited by 582
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  9. 2016GRAM: Graph-based Attention Model for Healthcare Representation Learningcited by 152
  10. 2018MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcarecited by 72
  11. 2020Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scalecited by 39
  12. 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 39
3 more
  1. 2020Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Taskscited by 21
  2. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  3. 2020Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Recordscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphscited by 3
  2. 2025KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddingscited by 1
  3. 2026AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarizationcited by 0