Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis
Seulkee Lee; Seonyoung Kang; Yeonghee Eun; Hong‐Hee Won; Hyungjin Kim; Jaejoon Lee; Eun‐Mi Koh; Hoon‐Suk Cha · 2021 · Arthritis Research & Therapy
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. METHODS: Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the traini
Abstract by Seulkee Lee; Seonyoung Kang; Yeonghee Eun; Hong‐Hee Won; Hyungjin Kim; Jaejoon Lee; Eun‐Mi Koh; Hoon‐Suk Cha, Arthritis Research & Therapy (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s13075-021-02635-3