Health Status Prediction with Local-Global Heterogeneous Behavior Graph

TL;DR

This paper introduces a novel local-global heterogeneous graph neural network model that effectively captures multi-source, complex temporal health data streams for accurate health status prediction, demonstrating superior performance on real-world datasets.

Contribution

The paper proposes a new local-global graph model combining heterogeneous GNNs and self-attention for health data analysis, addressing challenges of multi-source, temporal heterogeneity.

Findings

Model outperforms existing methods on StudentLife dataset

Effective learning of short-term local and long-term global health patterns

Demonstrates robustness and accuracy in health status prediction

Abstract

Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emerging of mobile devices provides the possibility to manage people's health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.