HDPL: Hypergraph-based Dynamic Prompting Learning for Incomplete Multimodal Medical Learning

Area of Science:

• Medical Informatics
• Machine Learning
• Artificial Intelligence

Background:

• Multimodal learning offers comprehensive insights in medicine but struggles with missing data, hindering predictive accuracy.
• Existing methods for handling missing modalities in multimodal learning face challenges like high computational costs or reliance on complete data, potentially skewing results.
• Transformer-based methods have limitations, particularly with structured medical data and processing multiple missing modalities.

Purpose of the Study:

• To develop an effective multimodal learning framework for incomplete medical data.
• To address the limitations of existing methods in handling missing modalities and computational complexity.
• To improve the accuracy and robustness of predictive models in the presence of missing medical data.

Main Methods:

• Introduced Hypergraph-based Dynamic Prompt Learning (HDPL), a novel framework for incomplete multimodal medical learning.
• Utilized a High-Order Hypergraph Embedding module to extract features from structured clinical data.
• Employed a Multimodal Medical Data Integrator for better modality fusion in transformers and a Dynamic Network Structure Optimization module to enhance performance and handle missing data.
• The framework comprises three modules: High-Order Hypergraph Embedding, Multimodal Medical Data Integrator, and Dynamic Network Structure Optimization.

Main Results:

• HDPL demonstrates efficiency and robustness in handling missing modalities in medical data.
• The proposed model effectively reduces training burdens compared to existing approaches.
• Experiments confirm the model's capability to improve predictive accuracy despite incomplete data.

Conclusions:

• HDPL offers a promising solution for multimodal medical learning with incomplete datasets.
• The framework successfully addresses challenges associated with missing modalities and computational costs.
• The study highlights the potential of hypergraph-based and dynamic learning approaches in medical AI.