在中低收入国家的卫生系统中，一个关键挑战是稀缺资源（尤其是基本药物）的高效且公平分配。由于高质量数据有限，传统数据驱动方法的应用受到限制。本文提出一种新型决策感知机器学习框架，用于基本药物分配；该框架进一步结合多任务学习以提升样本效率，并引入催化先验（catalytic priors）以保障分配的公平性。

A critical challenge in healthcare systems in low- and middle-income countries is the efficient and equitable allocation of scarce resources, particularly essential medicines1. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques2–5. Here we propose a novel decision-aware machine learning framework for the allocation of essential medicines, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation.