PAR2COX: Survival-Informed Tensor Decomposition for Phenotyping and Risk Prediction from Irregular Longitudinal Data

2026-09-20

AI Summary

为解决不规则纵向数据中的准确风险预测问题，提出PAR2COX方法，结合PARAFAC2分解与Cox模型，通过生存指导的表示学习提高风险分层效果。

Abstract

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.

Problem

• risk prediction
• longitudinal data
• censoring
• clinical decision making

Innovation

• PARAFAC2 decomposition
• Cox proportional hazards model
• survival-informed phenotype learning