Machine Learning to Predict Remission Between Six and 24 Months in Rheumatoid Arthritis: Insights from the JAK‐pot Collaboration

Zübeyir Salis; Denis Mongin; D. Choquette; Louis Coupal; C. Codreanu; Florenzo Iannone; Roberto Caporali; T.K. Kvien; Sella Provan; Ruth Fritsch‐Stork; Dan Nordström; Nina Trokovic; Karel Pavelka; Jakub Závada; A. R. A. RODRIGUES; Ziga Rotar; Prodromos Sidiropoulos; Irini Flouri; Céline Lamacchia; Michele Iudici; Delphine Courvoisier; K. Lauper; Axel Finckh

doi:10.1002/art.70165

Key Points

The research evaluates how well machine learning can predict remission in rheumatoid arthritis over a specified time frame.
Analyzed baseline data from 11 registries
Compared a ten-variable predictive model and logistic regression
Assessed performance of models in predicting remission
Prediction shows limited ability to discriminate between remission and non-remission
Model complexity offers minimal additional value
Logistic regression performs similarly to the ten-variable model

Implication

Predictive modeling evaluates remission in rheumatoid arthritis, suggesting simple models may be adequate.

Abstract

Using routinely collected baseline data across 11 registries, prediction of remission showed limited discrimination and was best suited to ruling out remission. Performance was similar for a ten-variable model and logistic regression, indicating limited incremental value of model complexity without richer predictors.

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Cite This Study

Salis et al. (2026) studied this question.

synapsesocial.com/papers/69d5f03374eaea4b11a79a79 https://doi.org/10.1002/art.70165

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Machine Learning to Predict Remission Between Six and 24 Months in Rheumatoid Arthritis: Insights from the JAK‐pot Collaboration

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine Learning to Predict Remission Between Six and 24 Months in Rheumatoid Arthritis: Insights from the JAK‐pot Collaboration

Key Points

Implication

Abstract

Discussion

Cite This Study

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