HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction

2026-02-05

AI Summary

This study addresses the limitations of existing healthcare visitation forecasting methods, which often neglect spatial dependencies among facilities and exhibit insufficient reliability during public health emergencies. To overcome these challenges, we propose a unified spatiotemporal context encoder that integrates both static and dynamic heterogeneous information. Our approach introduces Graph State Space Models (GraphMamba) to this domain for the first time, enabling hierarchical spatiotemporal modeling. Furthermore, we incorporate three uncertainty quantification mechanisms to enhance prediction robustness under anomalous conditions. Extensive experiments on four large-scale real-world datasets demonstrate that our method improves prediction accuracy by approximately 6.0% and boosts uncertainty quantification performance by 3.5% compared to current state-of-the-art models.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced machine learning methods being employed for better prediction performance, existing works usually formulate this task as a time-series forecasting problem without considering the intrinsic spatial dependencies of different types of healthcare facilities, and they also fail to provide reliable predictions under abnormal situations such as public emergencies. To advance existing research, we propose HealthMamba, an uncertainty-aware spatiotemporal framework for accurate and reliable healthcare facility visit prediction. HealthMamba comprises three key components: (i) a Unified Spatiotemporal Context Encoder that fuses heterogeneous static and dynamic information, (ii) a novel Graph State Space Model called GraphMamba for hierarchical spatiotemporal modeling, and (iii) a comprehensive uncertainty quantification module integrating three uncertainty quantification mechanisms for reliable prediction. We evaluate HealthMamba on four large-scale real-world datasets from California, New York, Texas, and Florida. Results show HealthMamba achieves around 6.0% improvement in prediction accuracy and 3.5% improvement in uncertainty quantification over state-of-the-art baselines.

Problem

• healthcare facility visit prediction
• spatiotemporal dependency
• uncertainty quantification
• public health emergencies
• resource allocation

Innovation

• Graph State Space Model
• Uncertainty Quantification
• Spatiotemporal Modeling
• Healthcare Visit Prediction
• GraphMamba