Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia

G Chandra*, JF Wang, P Siirtola, J Röning

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.
Original languageEnglish
Article number112
Number of pages7
JournalBMC Medical Research Methodology
Volume24
Issue number1
DOIs
Publication statusPublished - 11 May 2024

Keywords

  • Acute graft-versus-host disease
  • Allogeneic hematopoietic cell transplantation
  • Data size
  • Machine learning
  • Model performance
  • Orphan diseases
  • T-cell prolymphocytic leukemia

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