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.
@article{arxiv.2403.10158,
title = {Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights},
author = {Tobia Boschi and Francesca Bonin and Rodrigo Ordonez-Hurtado and Cécile Rousseau and Alessandra Pascale and John Dinsmore},
journal= {arXiv preprint arXiv:2403.10158},
year = {2024}
}