发表机构
University of Pennsylvania; National Medical Supplies Agency; Ministry of Health and Sanitation（宾夕法尼亚大学; 塞拉利昂国家医疗用品局; 塞拉利昂卫生与环卫部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低收入和中等收入国家基本药物分配难题，提出决策感知机器学习框架，结合多任务学习与催化先验，经与塞拉利昂政府合作实验，证明可提高药物获取效率，还在该国全国推广，展现低成本高效能。
AI 中文摘要
低收入和中等收入国家（LMICs）医疗系统面临的关键挑战是稀缺资源尤其是基本药物的高效公平分配。由于高质量数据有限，传统数据驱动技术适用性受限。本文提出一种用于基本药物分配的新型决策感知机器学习框架，利用多任务学习确保样本效率，利用催化先验确保公平分配。与塞拉利昂国家政府合作，将该系统作为决策支持工具进行了全国范围的交错部署。计量经济学评估发现，治疗地区分配产品的消费量估计增加了19%，证明了其在改善基本药物获取方面的有效性。该工具随后在全国范围内推广，覆盖约200万妇女和五岁以下儿童。研究表明机器学习方法能在资源受限的全球健康环境中以低成本提高效率。
英文摘要
A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million women and children under five. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.
Journal refNature, volume 653, issue 8116, 2026
DOI：10.1038/s41586-026-10433-7
