Survival Prediction in Allogeneic Haematopoietic Stem Cell Transplant Recipients Using Pre- and Post-Transplant Factors and Computational Intelligence.

ABSTRACT: Advancements in artificial intelligence (AI) predictive models have emerged as valuable tools for predicting survival outcomes in allogeneic haematopoietic stem cell transplantation (allo-HSCT). These models primarily focus on pre-transplant factors, while algorithms incorporating changes in patient's status post-allo-HSCT are lacking. The aim of this study was to develop a predictive soft computing model assessing survival outcomes in allo-HSCT recipients. In this study, we assembled a comprehensive database comprising of 564 consecutive adult patients who underwent allo-HSCT between 2015 and 2024. Our algorithm selectively considers critical parameters from the database, ranking and evaluating them based on their impact on patient outcomes. By utilising the Data Ensemble Refinement Greed

SUBMITTER: Asteris PG 

PROVIDER: S-EPMC12381535 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature