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DATA ANALYSIS IN 2019

Machine learning in rheumatology approaches the clinic

Nature Reviews Rheumatology volume 16, pages 69–70 (2020)Cite this article

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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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Fig. 1: Machine learning for precision medicine in rheumatology.

References

  1. 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).

    Article  CAS  Google Scholar 

  2. 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).

    Article  CAS  Google Scholar 

  3. 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).

    Article  CAS  Google Scholar 

  4. 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).

    Article  Google Scholar 

  5. Plenge, R. M. et al. Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge. Nat. Genet. 45, 468–469 (2013).

    Article  CAS  Google Scholar 

  6. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    Article  CAS  Google Scholar 

  7. 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).

    Article  Google Scholar 

  8. 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).

    Article  PubMed  Google Scholar 

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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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Authors and Affiliations

  1. Division Internal Medicine and Dermatology and Centre for Translational Immunology, University Medical Center Utrecht, Utrecht, Netherlands

    Aridaman Pandit & Timothy R. D. J. Radstake

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  1. Aridaman Pandit
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  2. Timothy R. D. J. Radstake
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Corresponding authors

Correspondence to Aridaman Pandit or Timothy R. D. J. Radstake.

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The authors declare no competing interests.

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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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