From machine learning to clinical practice: phenotypic clusters of anti-MDA5 antibody-positive dermatomyositis

A recent review written by McLeish et al. elegantly explains the use of machine learning techniques for the diagnosis, prognosis and treatment of idiopathic inflammatory myopathies (IIMs)[1]. The usefulness in clinical practice of such techniques, however, remains uncertain, as the authors themselves admit. A particular problem for the diagnosis, prognosis, and treatment of IIMs is their heterogeneity. Machine learning techniques have, according to McLeish et al.,‘the potential to effectively tackle the heterogeneity of IIMs, offering a promising avenue to enhance the accuracy of predicting disease progression and outcome.’As some of their examples, the authors summarize studies that used machine learning to generate phenotypic clusters of patients with anti-melanoma differentiation-associated protein 5 (MDA5) antibody-positive dermatomyositis (MDA5-DM). These studies were also summarized in an influential …