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 (Online ahead of print). doi: 10.1111/bjh.19615