Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon.
Marianne A MesselinkNadia M T RoodenrijsBram van EsCornelia A R Hulsbergen-VeelkenSebastiaan JongL Malin OvermarsLeon C ReteigSander C TanTjebbe TauberJacob M van LaarPaco M J WelsingSaskia HaitjemaPublished in: Arthritis research & therapy (2021)
During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.
Keyphrases
- rheumatoid arthritis patients
- healthcare
- disease activity
- end stage renal disease
- palliative care
- electronic health record
- clinical practice
- quality improvement
- newly diagnosed
- ejection fraction
- rheumatoid arthritis
- chronic kidney disease
- systemic lupus erythematosus
- pain management
- machine learning
- affordable care act
- current status
- patient reported outcomes
- chronic pain
- data analysis
- deep learning
- risk assessment
- patient reported