Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon

Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon

Abstract Background The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured a...

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Bibliographic Details
Main Authors: Marianne A. Messelink, Nadia M. T. Roodenrijs, Bram van Es, Cornelia A. R. Hulsbergen-Veelken, Sebastiaan Jong, L. Malin Overmars, Leon C. Reteig, Sander C. Tan, Tjebbe Tauber, Jacob M. van Laar, Paco M. J. Welsing, Saskia Haitjema
Format: Article
Language:English
Published: BMC 2021-07-01
Series:Arthritis Research & Therapy
Subjects:
Difficult-to-treat rheumatoid arthritis
Routine care data
Applied data analytics in medicine
Machine learning
Online Access:https://doi.org/10.1186/s13075-021-02560-5

Internet

https://doi.org/10.1186/s13075-021-02560-5

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