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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.10640 |
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| _version_ | 1866908588972179456 |
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| author | Pant, Piyush Suntoro, Marcellius William Siddiqua, Ayesha Sharif, Muhammad Shehryaar Ahmed, Daniyal |
| author_facet | Pant, Piyush Suntoro, Marcellius William Siddiqua, Ayesha Sharif, Muhammad Shehryaar Ahmed, Daniyal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equity-Aware Geospatial AI for Forecasting Demand-Driven Hospital Locations in Germany Pant, Piyush Suntoro, Marcellius William Siddiqua, Ayesha Sharif, Muhammad Shehryaar Ahmed, Daniyal Artificial Intelligence 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. |
| title | Equity-Aware Geospatial AI for Forecasting Demand-Driven Hospital Locations in Germany |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.10640 |