Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.

ABSTRACT:

Background

Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study aims to build a machine learning (ML) model to predict the progression from seronegative UA to RA using clinical and laboratory parameters.

Methods

KURAMA cohort (training dataset) and ANSWER cohort (validation dataset) were utilized. Patients with seronegative UA were selected based on specific inclusion and exclusion criteria. Clinical and laboratory parameters, including demographic data, acute phase reactants, autoantibodies, and physical examination findings, were collected. Various ML models, including a Feedforward Neural

SUBMITTER: Fujii T 

PROVIDER: S-EPMC11938622 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature