Could machine learning revolutionize how we treat immune thrombocytopenia?

Abstract

The absence of reliable biomarkers in immune thrombocytopenia (ITP) complicates treatment choice, necessitating a trial-and-error approach. Machine learning (ML) holds promise for transforming ITP treatment by analysing complex data to identify predictive factors, as demonstrated by Xu et al.'s study which developed ML-based models to predict responses to corticosteroids, rituximab and thrombopoietin receptor agonists. However, these models require external validation before can be adopted in clinical practice. Commentary on: Xu et al. A novel scoring model for predicting efficacy and guiding individualised treatment in immune thrombocytopenia. Br J Haematol 2024; 205:1108-1120.

Keywords: ITP; bleeding; machine learning.