GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis

TL;DR

GraphMMP is a novel graph neural network model that leverages mutual information and global fusion to improve multimodal medical prognosis accuracy across diverse datasets.

Contribution

The paper introduces GraphMMP, a two-stage GNN model that effectively models complex interactions in multimodal medical data using mutual information and global fusion.

Findings

Outperforms existing methods on liver prognosis datasets

Demonstrates superior accuracy on METABRIC study data

Effectively captures local and global dependencies across modalities

Abstract

In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenges lie in effectively modeling the complex interactions between heterogeneous data modalities with distinct characteristics while capturing both local and global dependencies across modalities. To address these challenges, this paper presents a two-stage multimodal prognosis model, GraphMMP, which is based on graph neural networks. The proposed model constructs feature graphs using mutual information and features a global fusion module built on Mamba, which significantly boosts prognosis performance. Empirical results show that GraphMMP surpasses existing methods on datasets related to liver prognosis and the METABRIC study, demonstrating its effectiveness in multimodal medical prognosis tasks.