Predicting rheumatoid arthritis in the middle-aged and older population using patient-reported outcomes: insights from the SHARE cohort.

Journal: International journal of medical informatics
PMID: 40209390

Abstract

BACKGROUND: In light of global population aging and the increasing prevalence of Rheumatoid Arthritis (RA) with age, strategies are needed to address this public health challenge. Machine learning (ML) may play a vital role in early identification of RA, allowing an early start of treatment, thereby reducing costs. This study aims first to identify potential variables related to RA, and second to explore and evaluate the potential of ML to identify RA patients in people over 50 years.

Authors

  • Fanji Qiu
    Movement Biomechanics, Institute of Sport Sciences, Humboldt-Universität zu Berlin, Unter Den Linden 6, 10099, Berlin, Germany. fanji.qiu@student.hu-berlin.de.
  • Rongrong Zhang
    Department of Psychiatry Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
  • Friedemann Schwenkreis
    Department of Business Information Systems, Baden-Wuerttemberg Cooperative State University Stuttgart, Paulinenstr. 50, 70178 Stuttgart, Germany.
  • Kirsten Legerlotz
    Movement Biomechanics, Institute of Sport Sciences, Humboldt-Universität zu Berlin, Unter Den Linden 6, 10099, Berlin, Germany.

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