Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation.

ABSTRACT:

Background

Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.

Methods and findings

We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art m

SUBMITTER: Yoon J 

PROVIDER: S-EPMC5874060 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature