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arXiv · 2403.10158

Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights

Tobia Boschi·Francesca Bonin·Rodrigo Ordonez-Hurtado·Cécile Rousseau·Alessandra Pascale·John Dinsmore
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Abstract

This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the complexities of multi-task and multi-modal learning in digital health and longitudinal studies. With the growing importance of health solutions to improve health care and social support, ensure healthy lives, and promote well-being at all ages, funGCN offers a unified approach to handle multivariate longitudinal data for multiple entities and ensures interpretability even with small sample sizes. Key innovations include task-specific embedding components that manage different data types, the ability to perform classification, regression, and forecasting, and the creation of a knowledge graph for insightful data interpretation. The efficacy of funGCN is validated through simulation experiments and a real-data application.

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Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado, Cécile Rousseau, Alessandra Pascale, John Dinsmore. 2024-03-15. Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights. https://doi.org/10.24963/ijcai.2024%2F795

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