PROMETHEUS 框架用多治疗因果排序优化人群健康分层
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
意大利团队把健康分层做成了“先治谁能获益最多”的排序问题，结合因果推断和 Siamese 排序网络，做医疗资源分配的可以看看。
论文提出 PROMETHEUS 框架，把人群健康分层建模为多治疗因果排序问题，目标是对患者-治疗机会按预期增量收益排序，而非追求精确的个体治疗效应估计。方法上将每种可行的护理强化方案与维持当前路径做对比，结合双重稳健因果信号、Siamese 神经排序模型和对比表示预训练，得到跨患者、跨护理方案的全局可比排序。该框架把临床需求、治疗收益和资源受限分配三者分开处理，可对接意大利 DM77 这类分层体系。实验包括合成数据和基于 ASL Benevento 真实管理数据协变量的半合成实验，评估了排序质量与分配价值，作者指出临床使用前还需真实治疗-结局数据验证。
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance on which care changes to prioritize under constrained resources. We present PROMETHEUS (Proactive Resource Optimization through Multi-treatment Effect-based Targeting in Healthcare Environments Using Stratification), a framework that formulates population health stratification as a multi-treatment causal ranking problem. Its objective is to order eligible patient-treatment opportunities by expected incremental benefit rather than obtain perfectly calibrated individual treatment-effect estimates. Each clinically admissible care intensification is compared with maintaining the patient's current pathway. Treatments are learned jointly to produce a globally comparable ranking across patients and care profiles, within domains sharing a reference condition, outcome, follow-up horizon, and benefit scale. This separates clinical need, treatment benefit, and resource-constrained allocation. PROMETHEUS combines doubly robust causal signals with a Siamese neural ranking model and contrastive representation pretraining. The shared representation captures similarities across care transitions, supporting prioritization under capacity constraints. Synthetic experiments and semi-synthetic experiments using real administrative covariates from ASL Benevento evaluate ranking quality, allocation value, robustness, and failure modes. The results support the framework's ability to recover generated benefit orderings and prioritize care opportunities in the evaluated settings. PROMETHEUS provides a preclinical approach to actionable stratification; validation with observed treatment-outcome data is required before clinical use.
