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.
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Population stratification for prediction of mortality in post-AKI patients
Institute of Applied Health Sciences, University of Aberdeen（阿伯丁大学应用卫生科学学院）；Institute of Mathematics and Statistics, University of Sao Paulo（圣保罗大学数学与统计学院）
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Bruno Kessler Institute（布鲁诺·凯斯勒研究所）；University of Trento（特伦托大学）；Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Institute for Next Generation Healthcare（下一代医疗保健研究所）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Azienda USL Centro “Santi”（圣蒂中央地方卫生局）；HK3 Lab（HK3实验室）
GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers
Mayo Clinic（梅奥诊所）；Kern Center for the Science of Health Care Delivery（克恩医疗保健科学中心）；Department of Artificial Intelligence and Informatics, Mayo Clinic（梅奥诊所人工智能与信息学系）
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
Sahlgrenska University Hospital（斯德哥尔摩大学医院）；Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg（计算机科学与工程系，查尔姆斯理工大学和乌普萨拉大学）；Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg（分子与临床医学系，斯德哥尔摩学院，乌普萨拉大学）；Department of Medicine, Geriatrics and Emergency Medicine, Sahlgrenska University Hospital（医学、老年医学和急诊医学系，斯德哥尔摩大学医院）；Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg（营养学与体育科学系，教育学院，乌普萨拉大学）；School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg（公共卫生与社区医学系，医学院，乌普萨拉大学）
Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease
University of Murcia（穆尔西亚大学）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经学研究所）；UK Dementia Research Institute（英国痴呆症研究所）；University of Cambridge（剑桥大学）；Washington University School of Medicine（华盛顿大学医学院）
Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data
Hood College（胡德学院）；Georgetown University（乔治敦大学）；University of Benin（贝宁大学）；NIST University（NIST大学）；AI CoLab: MedStar–Georgetown Collaborative Center for Artificial Intelligence in Healthcare Research and Education（人工智能合作实验室：梅斯达星 - 乔治敦医疗保健研究与教育人工智能协作中心）；MedStar Health Research Institute（梅斯达星健康研究所）；Albany State University（奥尔巴尼州立大学）
TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification
Rajshahi University of Engineering \& Technology Bangladesh；University of Dhaka Bangladesh；American International University of Bangladesh Bangladesh；Sonoma State University United States；Iowa State University United States；Rajshahi University of Engineering \& Technology；University of Dhaka；American International University of Bangladesh；Sonoma State University；Iowa State University
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
University of Warwick（华威大学）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院国民保健服务信托）；Guy’s and St Thomas’ NHS Trust（盖伊和圣托马斯国民保健服务信托）；King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
