Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach.

Journal: RMD open

Published Date: Nov 27, 2024

Abstract

OBJECTIVES: In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data in routine care.

Authors

Imke Redeker

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Styliani Tsiami

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Jan Eicker

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Uta Kiltz

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
David Kiefer

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Ioana Andreica

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Philipp Sewerin

Rheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, Germany.
Xenofon Baraliakos

Herne, Ruhr-University, Rheumazentrum Ruhrgebiet, Bochum, Germany.

Keywords

Adult Algorithms Axial Spondyloarthritis Back Pain Early Diagnosis Female Humans Machine Learning Male Middle Aged Random Forest ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (39608866)

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach.

Abstract

Authors

Keywords

External Resources

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