HealthMamba: 一种考虑不确定性的时空图状态空间模型用于有效可靠的医疗设施访问预测
HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction
AI总结：
本文提出HealthMamba，一种考虑不确定性的时空图状态空间模型，用于有效可靠的医疗设施访问预测。该模型包含三个关键组件：统一的时空上下文编码器、新的图状态空间模型GraphMamba以及综合的不确定性量化模块。实验结果显示，HealthMamba在预测准确性和不确定性量化方面分别比现有最佳基线提高了6.0%和3.5%。
摘要：
医疗设施访问预测对于优化医疗资源配置和 informing 公共卫生政策至关重要。尽管已经采用了先进的机器学习方法以提高预测性能，但现有工作通常将此任务视为时间序列预测问题，而没有考虑不同类型的医疗设施的内在空间依赖性，且在公共紧急情况等异常情况下也无法提供可靠的预测。为了推进现有研究，我们提出了HealthMamba，一种考虑不确定性的时空框架，用于准确且可靠的医疗设施访问预测。HealthMamba包含三个关键组件：(i) 一个统一的时空上下文编码器，融合异构的静态和动态信息，(ii) 一种新的图状态空间模型称为GraphMamba用于分层时空建模，(iii) 一个综合的不确定性量化模块，整合三种不确定性量化机制以实现可靠的预测。我们在四个大规模真实世界数据集上评估了HealthMamba，这些数据集来自加州、纽约、得克萨斯州和佛罗里达州。结果表明，HealthMamba在预测准确性和不确定性量化方面分别比现有最佳基线提高了6.0%和3.5%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
