# Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph

arXiv: https://arxiv.org/abs/2411.19742 · HTML: https://mlsanity.com/paper/2411.19742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [Deepr: A Convolutional Net for Medical Records](https://mlsanity.com/paper/1607.07519/) (2016)
- [Predicting Patient Outcomes with Graph Representation Learning](https://mlsanity.com/paper/2101.03940/) (2021)
- [Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission](https://mlsanity.com/paper/2204.06766/) (2022)

## Built on by (most cited first, 2 of 2)

- [From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification](https://mlsanity.com/paper/2509.21341/) (2025)
- [Similarity-Based Self-Construct Graph Model for Predicting Patient Criticalness Using Graph Neural Networks and EHR Data](https://mlsanity.com/paper/2508.00615/) (2025)

## Abstract (arXiv)

Objective: In modern healthcare, accurately predicting diseases is a crucial matter. This study introduces a novel approach using graph neural networks (GNNs) and a Graph Transformer (GT) to predict the incidence of heart failure (HF) on a patient similarity graph at the next hospital visit. Materials and Methods: We used electronic health records (EHR) from the MIMIC-III dataset and applied the K-Nearest Neighbors (KNN) algorithm to create a patient similarity graph using embeddings from diagnoses, procedures, and medications. Three models - GraphSAGE, Graph Attention Network (GAT), and Graph Transformer (GT) - were implemented to predict HF incidence. Model performance was evaluated using F1 score, AUROC, and AUPRC metrics, and results were compared against baseline algorithms. An interpretability analysis was performed to understand the model's decision-making process. Results: The GT model demonstrated the best performance (F1 score: 0.5361, AUROC: 0.7925, AUPRC: 0.5168). Although the Random Forest (RF) baseline achieved a similar AUPRC value, the GT model offered enhanced interpretability due to the use of patient relationships in the graph structure. A joint analysis of attention weights, graph connectivity, and clinical features provided insight into model predictions across different classification groups. Discussion and Conclusion: Graph-based approaches such as GNNs provide an effective framework for predicting HF. By leveraging a patient similarity graph, GNNs can capture complex relationships in EHR data, potentially improving prediction accuracy and clinical interpretability.