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
Sieberts, S. K. et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat. Commun. 7, 12460 (2016).
Kim, K. J. et al. Compendium of synovial signatures identifies pathologic characteristics for predicting treatment response in rheumatoid arthritis patients. Clin. Immunol. 202, 1–10 (2019).
Guan, Y. et al. Machine learning to predict anti-TNF drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheum. 71, 1987–1996 (2019).
Eng, S. W. M. et al. Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: a prospective study with multilayer non-negative matrix factorization. PLOS Med. 16, e1002750 (2019).
Plenge, R. M. et al. Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge. Nat. Genet. 45, 468–469 (2013).
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Kedra, J. et al. Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations. RMD Open 5, e001004 (2019).
Gossec, L. et al. EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases. Ann. Rheum. Dis. https://doi.org/10.1136/annrheumdis-2019-215694 (2019).
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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