Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach
Imke Redeker, Styliani Tsiami, Jan Eicker, Uta Kiltz, David Kiefer, Ioana Andreica, Philipp Sewerin, Xenofon Baraliakos · 2024
Read Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach by Imke Redeker, Styliani Tsiami, Jan Eicker, Uta Kiltz, David Kiefer, Ioana Andreica, Philipp Sewerin, Xenofon Baraliakos (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11603692.txt.
Description
Imke Redeker and co-authors build a diagnostic model for back pain in a 2024 paper. Axial spondyloarthritis inflames the spine and is often found years late, since no firm diagnostic rule exists. The team asked whether routine clinic data could sort such patients from others with chronic back pain.
Records of more than nine hundred patients from a specialist practice fed a random forest model. On held-out test data the model scored high on accuracy and caught most true cases. One gene marker tied to the disease carried the most weight in the model. Slow onset of pain and joint erosion on scans ranked next. The authors say the tool could cut diagnostic delay in daily care, pending wider tests.
Catalog record
| CREATOR | Imke Redeker, Styliani Tsiami, Jan Eicker, Uta Kiltz, David Kiefer, Ioana Andreica, Philipp Sewerin, Xenofon Baraliakos |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |