Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

ABSTRACT:

Abstract

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183 patients with MF who underwent first allo-HCT between 2005 and 2020 at European Society for Blood and Marrow Transplantation centers, we examined different machine learning (ML) models to predict overall survival after transplant. The cohort was divided into a training set (75%) and a test set (25%) for model validation. A random survival forests (RSF) model was developed based on 10 variables: patient age, comorbidity index, performance status, blood blasts, hemoglobin, leukocytes, platelets, donor type, conditioning intensit

SUBMITTER: Hernandez-Boluda JC 

PROVIDER: S-EPMC12824666 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature