arXiv：cs.LG· Rocco Zaccagnino, Martina Girolamo, Alberto Lombardi, Delfina Malandrino, Donatello Telesca, Alessia Ture·· 3 小时前
PROMETHEUS：面向人群健康分层的多治疗因果排序框架
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
AI 导读
PROMETHEUS 将人群健康分层建模为多治疗因果排序问题，按预期增量收益对符合条件的患者-治疗机会排序，而非追求精确的个体治疗效果估计。该框架结合双重稳健因果信号、Siamese 神经排序模型与对比表示预训练，在共享参考病症、结局、随访窗口和收益量纲的领域内实现全局可比的排序，从而分离临床需求、治疗收益与资源受限分配。
正文
Abstract: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.
| Comments: | 19 pages, 1 figure, 19 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19782 [cs.LG] |
| (or arXiv:2610.19782v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19782 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rocco Zaccagnino [view email]
[v1]
Wed, 14 Oct 2026 08:31:07 UTC (56 KB)
来源：arXiv：cs.LG · arxiv.org