Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.
Grant W CannonJorge RojasAndreas ReimoldTed R MikulsDebra BergmanBrian C SauerPublished in: ACR open rheumatology (2019)
The automated text processing approach is highly efficient and performed as well as the manual extraction approach. This advance has the potential for significant improvements in the collection, documentation, and extraction of these data to support clinical practice and outcomes research relevant to RA as well as the potential for broader application to other health conditions.
Keyphrases
- electronic health record
- disease activity
- rheumatoid arthritis
- highly efficient
- systemic lupus erythematosus
- machine learning
- rheumatoid arthritis patients
- ankylosing spondylitis
- deep learning
- clinical practice
- clinical decision support
- high throughput
- juvenile idiopathic arthritis
- adverse drug
- healthcare
- human health
- public health
- mental health
- interstitial lung disease
- risk assessment
- type diabetes
- adipose tissue
- metabolic syndrome
- big data
- insulin resistance
- social media