用时空状态空间模型提升医疗访视预测准确率与可靠性
HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction
- 融合静态动态信息的统一时空编码器，捕捉设施间空间依赖
- 在四地真实数据上准确率提升6.0%，不确定性量化提升3.5%
- 适合需可靠预测的公共卫生应急场景，支持决策风险评估
医疗设施访视预测对优化资源配置和制定公共政策至关重要。现有方法多将此任务视为时间序列预测，忽略不同医疗设施间的固有空间依赖，且在突发公共事件等异常情况下难以提供可靠预测。为此，我们提出 HealthMamba——一种具备不确定性感知能力的时空图状态空间模型。该框架包含三部分：(i) 融合异构静态与动态信息的统一时空上下文编码器；(ii) 用于分层时空建模的新型图状态空间模型 GraphMamba；(iii) 集成三种不确定性量化机制的综合不确定性量化模块。我们在加州、纽约州、德克萨斯州和佛罗里达州四个大规模真实数据集上进行了评估。结果表明，HealthMamba 在预测准确率上较先进基线平均提升约6.0%，在不确定性量化方面提升3.5%。
原文摘要 · Abstract (English)
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.
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