用大模型融合专家意见，优化埃塞俄比亚医疗点升级优先级
Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning
- 结合优化算法与大模型，将专家语言建议转化为可执行方案
- 在三个地区实测中显著提升覆盖人口，且保持理论最优保障
- 适合政策制定者和公共卫生规划人员参考使用
埃塞俄比亚卫生部正升级基层卫生站以改善农村地区医疗服务可及性。受限于资源，需科学决策哪些设施优先升级，以最大化覆盖人群，并兼顾多元专家与利益相关方的偏好。我们与埃塞俄比亚公共卫生研究所及卫生部合作，提出一种混合框架——大语言模型与扩展贪心算法（LEG）。该框架将具有理论保证的群体覆盖优化算法，与基于LLM的迭代精炼机制结合，实现人机对齐，确保方案既反映专家定性指导，又维持覆盖率保证。在埃塞三个地区的实际数据上验证表明，该框架有效支持公平、数据驱动的卫生系统规划。
原文摘要 · Abstract (English)
Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritization of which facilities to upgrade to maximize population coverage while accounting for diverse expert and stakeholder preferences. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we propose a hybrid framework that systematically integrates expert knowledge with optimization techniques. Classical optimization methods provide theoretical guarantees but require explicit, quantitative objectives, whereas stakeholder criteria are often articulated in natural language and difficult to formalize. To bridge these domains, we develop the Large language model and Extended Greedy (LEG) framework. Our framework combines a provable approximation algorithm for population coverage optimization with LLM-driven iterative refinement that incorporates human-AI alignment to ensure solutions reflect expert qualitative guidance while preserving coverage guarantees. Experiments on real-world data from three Ethiopian regions demonstrate the framework's effectiveness and its potential to inform equitable, data-driven health system planning.
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