# Graph Transformer with Disease Subgraph Positional Encoding for Improved Comorbidity Prediction

arXiv: https://arxiv.org/abs/2503.03046 · HTML: https://mlsanity.com/paper/2503.03046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Graph-Bert: Only Attention is Needed for Learning Graph Representations](https://mlsanity.com/paper/2001.05140/) (2020)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [One-Hot Graph Encoder Embedding](https://mlsanity.com/paper/2109.13098/) (2021)
- [Efficient Graph Encoder Embedding for Large Sparse Graphs in Python](https://mlsanity.com/paper/2406.03726/) (2024)

## Abstract (arXiv)

Comorbidity, the co-occurrence of multiple medical conditions in a single patient, profoundly impacts disease management and outcomes. Understanding these complex interconnections is crucial, especially in contexts where comorbidities exacerbate outcomes. Leveraging insights from the human interactome (HI) and advancements in graph-based methodologies, this study introduces Transformer with Subgraph Positional Encoding (TSPE) for disease comorbidity prediction. Inspired by Biologically Supervised Embedding (BSE), TSPE employs Transformer's attention mechanisms and Subgraph Positional Encoding (SPE) to capture interactions between nodes and disease associations. Our proposed SPE proves more effective than LPE, as used in Dwivedi et al.'s Graph Transformer, underscoring the importance of integrating clustering and disease-specific information for improved predictive accuracy. Evaluated on real clinical benchmark datasets (RR0 and RR1), TSPE demonstrates substantial performance enhancements over the state-of-the-art method, achieving up to 28.24% higher ROC AUC and 4.93% higher accuracy. This method shows promise for adaptation to other complex graph-based tasks and applications. The source code is available in the GitHub repository at: https://github.com/xihan-qin/TSPE-GraphTransformer.