Multi-context graph neural networks for enhanced multivariate time-series analysis in healthcare
Creators: name: Nocera, Luciano
name: Hajisafi, Arash
name: Siampou, Maria Despoina
name: Azarijoo, Bita
name: Shahabi, Cyrus
Publisher: name: F1000 Research Limited
Year: 2024

description: <b>Multi-Context Graph Neural Networks for Enhanced Multivariate Time-Series Analysis in Healthcare</b><br>
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Effective multivariate time-series (MTS) analysis in healthcare is crucial for various medical tasks and requires capturing complex inter-variable relationships accurately. Previous methods often fail to model these relationships adequately, leading to poor predictions, especially when training data size is limited. Our research introduces a series of Graph Neural Networks (GNNs) designed to overcome these limitations by explicitly modeling MTS as graphs, where each variable is a node connected by edges representing multi-context inter-variable relationships.<br>
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Our first work, Busyness Graph Neural Network (BysGNN), is a temporal GNN that integrates semantical, temporal, spatial, and taxonomical data to model interactions between Points of Interest (POIs). This model was particularly effective during the COVID-19 pandemic for forecasting POI visits to set occupancy restrictions in urban settings, demonstrating substantial improvements over previous forecasting models by capturing complex multi-context correlations.<br>
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Building upon BysGNN, we developed NeuroGNN, which extends the graph-based approach to EEG data analysis. NeuroGNN dynamically constructs graphs that reflect the evolving relationships between EEG electrodes and associated brain regions, significantly enhancing seizure detection and classification. The model’s ability to incorporate diverse contextual data improves its capability to classify rare seizure types with limited samples, addressing a significant challenge in medical diagnostics.<br>
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Our latest work, WaveGNN, further extends the application of GNNs by addressing irregular MTS data common in healthcare (e.g., unaligned and incomplete measurements of vital signs). WaveGNN integrates additional data modalities, including Electronic Health Record (EHR) notes, to build a robust graph representation that maintains accuracy nearly equivalent to scenarios with complete data, even in the presence of significant data gaps. This substantially improves over existing methods, which typically suffer performance degradation under similar conditions.<br>
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These three works demonstrate the potential of GNNs in healthcare MTS analysis to provide a robust framework for accurately predicting medical outcomes and enhancing diagnosis.<br>
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<b>BysGNN:</b><br>
Hajisafi, Arash, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, and Cyrus Shahabi. "Learning dynamic graphs from all contextual information for accurate point-of-interest visit forecasting." In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pp. 1-12. 2023.<br>
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<b>NeuroGNN:</b><br>
Hajisafi, Arash, Haowen Lin, Yao-Yi Chiang, and Cyrus Shahabi. "Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data." In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 207-220. Singapore: Springer Nature Singapore, 2024.