Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.

ABSTRACT: Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calculations. The primary objective of this study was to establish and validate the machine learning-dependent index for predicting aGVHD. This was a retrospective cohort study that involved analyzing databases of adult HSCT patients in Japan. The alternating decision tree (ADTree) machine learning algorithm was applied to develop models using the training cohort (70%). The ADTree algorithm was confirmed using the hazard model on data from the validation cohort (30%). Data from 26 695 HSCT patients transplanted from allogeneic donors between 1992 and

SUBMITTER: Arai Y 

PROVIDER: S-EPMC6880900 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature