Machine Learning in Rheumatic Diseases.

Journal: Clinical reviews in allergy & immunology
Published Date:

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.

Authors

  • Mengdi Jiang
    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.
  • Yueting Li
    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.
  • Chendan Jiang
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, China.
  • Lidan Zhao
    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. zhaolidan@hotmail.com.
  • Xuan Zhang
  • Peter E Lipsky
    RILITE Research Institute and AMPEL BioSolutions, 250 W Main St, Ste 300, Charlottesville, VA, 22902, USA.