{"type":"ScholarlyArticle","title":"Functional Graph Convolutional Networks: A Unified Multi-task and Multi-modal Learning Framework to Facilitate Health and Social-Care Insights","authors":[{"name":"Tobia Boschi","slug":"tobia-boschi","given":"Tobia","family":"Boschi","affiliation":"IBM Research Europe , Dublin"},{"name":"Francesca Bonin","slug":"francesca-bonin","given":"Francesca","family":"Bonin","affiliation":"IBM Research Europe , Dublin"},{"name":"Rodrigo Ordonez-Hurtado","slug":"rodrigo-ordonez-hurtado","given":"Rodrigo","family":"Ordonez-Hurtado","affiliation":"IBM Research Europe , Dublin"},{"name":"Cécile Rousseau","slug":"cecile-rousseau","given":"Cécile","family":"Rousseau","affiliation":"École Centrale de Lille"},{"name":"Alessandra Pascale","slug":"alessandra-pascale","given":"Alessandra","family":"Pascale","affiliation":"IBM Research Europe , Dublin"},{"name":"John Dinsmore","slug":"john-dinsmore","given":"John","family":"Dinsmore","affiliation":"Trinity Centre for Practice and Healthcare Innovation , Trinity College of Dublin"}],"abstract":"This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks\r\nto 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. funGCN source code is publicly available at https://github.com/IBM/funGCN.","doi":"10.24963/ijcai.2024/795","url":"https://ijcai.org/proceedings/2024/795","pdf":"https://ijcai.org/proceedings/2024/0795.pdf","markdown":"https://ijcai.org/proceedings/2024/795.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/795","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":795,"pages":{"first":7188,"last":7196},"keywords":["Machine Learning: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Bergmann et al., 2017] Michael Bergmann, Thorsten Kneip, Giuseppe De Luca, and Annette Scherpenzeel. 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