发表机构
University of Padua; The University of Texas at Dallas; Georgia Institute of Technology（帕多瓦大学; 德克萨斯大学达拉斯分校; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出PAR2COX框架，融合PARAFAC2分解与Cox模型，利用患者潜在因子进行生存引导的表型学习，在不规则纵向数据上提升风险预测与分层性能。
AI 中文摘要
准确的风险预测对于临床决策、干预规划、治疗和移植分配至关重要。然而，纵向临床数据通常是不规则的，并且受到删失的影响。我们提出了PAR2COX，一个将PARAFAC2分解与Cox比例风险模型相结合的联合框架，在似然函数中使用患者特定的潜在因子作为协变量。所提出的交替优化框架联合估计表型和生存参数，实现了生存引导的表示学习。PAR2COX同时适用于具有已观测结果的历史患者和结果尚不明确的当前患者。基于MIMIC-IV数据的数值实验和案例研究表明，与现有方法相比，该方法改善了风险分层，凸显了生存信息表型学习的价值。
英文摘要
Accurate risk prediction is crucial for clinical-decision making, intervention planning, treatment and transplant allocation. However, longitudinal clinical data are often irregular and subject to censoring. We propose PAR2COX, a joint framework that integrates PARAFAC2 decomposition with Cox proportional hazards model, using patient-specific latent factors as covariates in the likelihood. The proposed alternating optimization framework jointly estimates phenotypes and survival parameters, enabling survival-guided representation learning. PAR2COX accommodates both historical patients with observed outcomes and current patients whose outcomes remain unknown. Numerical experiments and a case study based on MIMIC-IV data demonstrate improved risk stratification compared with existing approaches, highlighting the value of survival-informed phenotype learning.
Comments33 pages, 5 figures
