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Machine Learning in Rheumatic Diseases

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Abstract

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients’ stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

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Funding

This study was supported by grants from the National Natural Science Foundation of China (81788101, 81630044), Chinese Academy of Medical Science Innovation Fund for Medical Sciences (CIFMS2016-12M-1-003, 2017-12M-1-008, 2017-I2M-3-011, 2016-12M-1-008), Beijing Capital Health Development Fund (2020-2-4019), and Grant from Medical Epigenetics Research Center, Chinese Academy of Medical Sciences (2017PT31035).

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Author notes

  1. Mengdi Jiang and Yueting Li contributed equally to this work.

Authors and Affiliations

  1. Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College; The Ministry of Education Key Laboratory, Beijing, 100730, China

    Mengdi Jiang, Yueting Li & Lidan Zhao

  2. Clinical Immunology Centre, Medical Epigenetics Research Centre, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College; The Ministry of Education Key Laboratory, Beijing, 100730, China

    Mengdi Jiang, Yueting Li & Xuan Zhang

  3. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Chendan Jiang

  4. RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, USA

    Peter E Lipsky

Authors
  1. Mengdi Jiang
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  2. Yueting Li
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  3. Chendan Jiang
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  4. Lidan Zhao
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  5. Xuan Zhang
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  6. Peter E Lipsky
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Contributions

JMD and LYT wrote the article. JCD revised the figures. ZLD, ZX, and Lipsky P.E. revised the manuscript. All authors researched the data for the article, made substantial contributions to the content, and edited the manuscript.

Corresponding authors

Correspondence to Lidan Zhao or Xuan Zhang.

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Jiang, M., Li, Y., Jiang, C. et al. Machine Learning in Rheumatic Diseases. Clinic Rev Allerg Immunol 60, 96–110 (2021). https://doi.org/10.1007/s12016-020-08805-6

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