Machine learning and high-throughput technologies hold promise for the classification, diagnosis and treatment of patients with rheumatic diseases, with the ultimate goal of precision medicine. Several studies in 2019 highlight the feasibility and clinical utility of using machine learning in rheumatology to stratify patients and/or predict treatment responses.
Key advances
Synovial transcriptomic analysis and a machine learning-based approach identified subgroups of patients with rheumatoid arthritis (RA) and enabled the development of a model that could predict treatment response to TNF inhibition2.
A machine learning-based model, developed as part of a crowdsourced open competition, could predict changes in disease activity and predict the treatment response of patients with RA3.
Analysis of patterns of joint involvement and a machine learning-based approach enabled the development of a model that could predict the disease course of patients with juvenile idiopathic arthritis4.
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References
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Acknowledgements
The work of A.P. is supported by Netherlands Organisation for Scientific Research (NWO) (Grant number 016.Veni.178.027).
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Pandit, A., Radstake, T.R.D.J. Machine learning in rheumatology approaches the clinic. Nat Rev Rheumatol 16, 69–70 (2020). https://doi.org/10.1038/s41584-019-0361-0
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DOI: https://doi.org/10.1038/s41584-019-0361-0
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