Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights
<|reference_start|>Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights: 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.<|reference_end|>
@article{boschi2024functional,
    title={Functional Graph Convolutional Networks: A unified multi-task and
  multi-modal learning framework to facilitate health and social-care insights},
    author={Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado, C'ecile
  Rousseau, Alessandra Pascale, and John Dinsmore},
    journal={IJCAI 2024},
    year={2024},
    doi={10.24963/ijcai.2024/795},
    archivePrefix={arXiv},
    eprint={2403.10158},
    primaryClass={cs.LG cs.AI}
}