Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912020266221568 |
|---|---|
| author | Boschi, Tobia Bonin, Francesca Ordonez-Hurtado, Rodrigo Rousseau, Cécile Pascale, Alessandra Dinsmore, John |
| author_facet | Boschi, Tobia Bonin, Francesca Ordonez-Hurtado, Rodrigo Rousseau, Cécile Pascale, Alessandra Dinsmore, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights Boschi, Tobia Bonin, Francesca Ordonez-Hurtado, Rodrigo Rousseau, Cécile Pascale, Alessandra Dinsmore, John Machine Learning Artificial Intelligence 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. |
| title | Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.10158 |