With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using hypergraph computation for disease prediction. Hypergraphs allow for the representation of higher-order relations, called hyperedges, spanning possibly more than two nodes to capture complex correlations within multimodal medical data and patients' characteristics. We propose a dynamic bi-clustering approach to learn a multi-hypergraph structure based on node embedding to model high-order multimodal patient interaction. We have conducted experiments on benchmark real-world datasets for Alzheimer's Disease and Autism Spectrum Disorder prediction. Experimental results demonstrate that the proposed Hypergraph Neural Network method outperforms state-of-the-art methods.
Bollengier, M, Diaz Berenguer, A & Sahli, H 2024, Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data. in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 1-5, 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 15/07/24. https://doi.org/10.1109/EMBC53108.2024.10782538
Bollengier, M., Diaz Berenguer, A., & Sahli, H. (2024). Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings (pp. 1-5). (Annual International Conference of the IEEE Engineering in Medicine and Biology Society). IEEE. https://doi.org/10.1109/EMBC53108.2024.10782538
@inproceedings{ce53673defe7414ab3d296f4ab76cdb6,
title = "Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data",
abstract = "With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using hypergraph computation for disease prediction. Hypergraphs allow for the representation of higher-order relations, called hyperedges, spanning possibly more than two nodes to capture complex correlations within multimodal medical data and patients' characteristics. We propose a dynamic bi-clustering approach to learn a multi-hypergraph structure based on node embedding to model high-order multimodal patient interaction. We have conducted experiments on benchmark real-world datasets for Alzheimer's Disease and Autism Spectrum Disorder prediction. Experimental results demonstrate that the proposed Hypergraph Neural Network method outperforms state-of-the-art methods.",
author = "Maxime Bollengier and \{Diaz Berenguer\}, Abel and Hichem Sahli",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ; Conference date: 15-07-2024 Through 19-07-2024",
year = "2024",
month = dec,
day = "17",
doi = "10.1109/EMBC53108.2024.10782538",
language = "English",
isbn = "979-8-3503-7150-5",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
publisher = "IEEE",
pages = "1--5",
booktitle = "2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings",
url = "https://embc.embs.org/2024/",
}