Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.

ABSTRACT:

Abstract

Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT. The model was developed using clinical data from children and young adults treated at a single academic quaternary-care referral center. The model was created using a randomly split training data set (70% of the cohor

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10844815 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature