Current Status of Machine Learning for Precision Rheumatology: Are We There Yet?

Abstract

Autoimmune rheumatic diseases are often characterised by heterogeneity in presentation. The traditional approach to diseases guided by their phenotype may be suboptimal with the advent of precision medicine. Precision medicine is the integration and application of multiomics to predict the best-performing drug and its toxicity profile to derive optimal benefits. With novel drug discoveries and an expanding therapeutic armamentarium, it potentially aids in clinical and therapeutic decision-making, while saving time and averting adverse events. However, multiomics comes with ‘big data’, and owing to the costs, the sample size is usually small. Machine learning (ML) plays an important role in these scenarios where conventional statistics fall short. So, by integrating clinical data with the data from -omics, ML models can be built, which can accurately predict the clinical factors or even novel biomarkers that predict response. This approach has a potential for great benefit as valuable time or the ‘therapeutic window of opportunity’ would be saved, with fewer adverse events, eventually translating to lower damage accrual and better outcomes. Most of the evidence for the use of ML in precision rheumatology comes from rheumatoid arthritis and the factors predicting response to various drugs, including tumour necrosis factor inhibitors. This approach also has its limitations such as the lack of generalizability and the current scarcity of longitudinal data. These models must be tested in larger cohorts and population-based studies for validation, failing which there is a risk of apparent identification of multiple ‘novel’ biomarkers that may or may not be mechanistic.