A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.

ABSTRACT:

Background

Forecasting acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (HSCT) is highly challenging with conventional statistical techniques due to complex parameters and their interactions. The primary object of this study was to establish a convolutional neural network (CNN)-based prediction model for aGVHD.

Method

We analyzed adult patients who underwent allogeneic HSCT between 2008 and 2018, using the Japanese nationwide registry database. The CNN algorithm, equipped with a natural language processing technique and an interpretable explanation algorithm, was applied to develop and validate prediction models.

Results

Here, we evaluate 18,763 patients between 16 and 80 years of age (median, 50 years). In total, grade

SUBMITTER: Jo T 

PROVIDER: S-EPMC10188562 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature