HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction
arXiv:2602.05286v3 Announce Type: replace 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 propos
The increasing availability of healthcare data and advanced machine learning techniques, particularly in spatio-temporal modeling, enables more sophisticated predictive solutions for facility management and public health planning.
This development offers a more reliable and nuanced approach to healthcare resource allocation by considering spatial dependencies and uncertainty, moving beyond simplistic time-series forecasting.
Healthcare facility visit prediction moves from general time-series models to more specific, location-aware, and uncertainty-aware models, improving accuracy and utility for planning.
- · Healthcare administrators
- · Public health policy makers
- · Smart city planning
- · AI/ML healthcare solution providers
- · Healthcare systems relying on rudimentary forecasting
- · Patients experiencing resource misallocation
Improved healthcare resource allocation and better preparation for public health emergencies become possible.
This could lead to more efficient healthcare operations, potentially reducing costs and improving patient outcomes at a regional level.
The enhanced predictability in healthcare could attract more R&D investment into integrated spatial and temporal AI models for critical infrastructures.
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