ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Health Education and Promotion
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1640808
This article is part of the Research TopicLeveraging Information Systems and Artificial Intelligence for Public Health AdvancementsView all 11 articles
A Hypergraph Convolution-Based Intelligent Healthcare Platform for Aging Population Management
Provisionally accepted
- 1The Academy of Social Sciences on Water Research in the New Era, North China, zhenghzou, China
- 2North China University of Water Resources and Electric Power, Zhengzhou, China
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Population aging intensifies demands on healthcare systems, particularly in managing chronic diseases among older adults. To address limitations in multimodal data integration and complex disease association analysis, this study proposes an intelligent healthcare platform leveraging Hypergraph Convolutional Networks (HGCN). The framework fuses real-time multimodal data-physiological indicators, behavioral logs, and environmental parameters-from wearable/Internet of Things (IoT) devices into a dynamic medical knowledge graph. By integrating hypergraph convolution with hierarchical feature learning, it enables precise health analysis and cross-institutional collaboration. Experiments demonstrate superior disease-risk prediction (87.26% accuracy, 0.831 F1-score) and robust system performance (100% request success rate under 480 concurrent users, minimal latency). This work advances intelligent elderly care by optimizing resource allocation and enabling personalized health management.
Keywords: Population aging, Intelligent healthcare, integrated medical and aged care services, Internet of thing, hypergraph convolutional network
Received: 04 Jun 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Li, Hou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wenjie Li, The Academy of Social Sciences on Water Research in the New Era, North China, zhenghzou, China
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