# Self-Attention Equipped Graph Convolutions for Disease Prediction

arXiv: https://arxiv.org/abs/1812.09954 · HTML: https://mlsanity.com/paper/1812.09954/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)
- [BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification](https://mlsanity.com/paper/2504.16096/) (2025)
- [AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/2106.08732/) (2021)
- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions](https://mlsanity.com/paper/2203.12616/) (2022)
- [Brain dynamics via Cumulative Auto-Regressive Self-Attention](https://mlsanity.com/paper/2111.01271/) (2021)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2107.00206/) (2021)
- [Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery](https://mlsanity.com/paper/2303.03388/) (2023)

## Abstract (arXiv)

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such diverse data gives complementary information about the patient's condition to make an informed diagnosis. A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction. We propose a graph convolution based deep model which takes into account the distinctiveness of each element of the multi-modal data. We incorporate a novel self-attention layer, which weights every element of the demographic data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and performance when compared to state-of-the-art methods. Our method outperforms other methods with a significant margin.