用智能算法预测德国医院需求，确保资源分配更公平
Equity-Aware Geospatial AI for Forecasting Demand-Driven Hospital Locations in Germany
- 构建综合公平指数，融合人口变化与医疗资源
- 在预算和交通时间限制下优化床位分配与选址
- 适合政策制定者用于长期医疗规划
本文提出EA-GeoAI框架，通过整合地区级人口变迁、老龄化密度及基础设施平衡，构建统一的公平指数，实现对德国至2030年需求的预测与医院规划。采用可解释的智能体优化器，在预算与出行时间约束下，分配床位并识别新设医疗机构位置，以最小化未满足需求。该方法融合地理人工智能、长期预测与公平性评估，为政策制定者提供可执行的建议。
原文摘要 · Abstract (English)
This paper presents EA-GeoAI, an integrated framework for demand forecasting and equitable hospital planning in Germany through 2030. We combine district-level demographic shifts, aging population density, and infrastructure balances into a unified Equity Index. An interpretable Agentic AI optimizer then allocates beds and identifies new facility sites to minimize unmet need under budget and travel-time constraints. This approach bridges GeoAI, long-term forecasting, and equity measurement to deliver actionable recommendations for policymakers.
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