Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity.
Jeffrey R CurtisYujie SuShawn BlackStephen XuWayne LangholffClifton O BinghamShelly KafkaFenglong XiePublished in: ACR open rheumatology (2022)
ML methods coupled with longitudinal PRO data appear useful and can achieve reasonable accuracy in classifying LDA among patients starting a new biologic. This approach has promise for real-world evidence generation in the common circumstance when physician-derived disease activity data are not available yet PRO measures are.
Keyphrases
- disease activity
- rheumatoid arthritis
- patient reported outcomes
- big data
- rheumatoid arthritis patients
- systemic lupus erythematosus
- machine learning
- ankylosing spondylitis
- primary care
- juvenile idiopathic arthritis
- emergency department
- electronic health record
- interstitial lung disease
- anti inflammatory
- artificial intelligence
- data analysis