An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs. In this Review, the authors provide an introduction to machine learning and discuss the use of this approach in rheumatic autoimmune inflammatory diseases, including the classification of patients based on medical records or molecular characteristics, identification of novel biomarkers or drug repurposing candidates and prediction of disease progression or onset.

Automated summary

Machine learning is increasingly used in biomedicine, particularly in rheumatology, for classifying patients with RAIDs. While ML can identify relationships in data that were previously unrecognized, limitations such as sample size, accuracy of labelling, and lack of external validation datasets exist.