Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation.

ABSTRACT: Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a clas

SUBMITTER: Shourabizadeh H 

PROVIDER: S-EPMC11741629 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature