Leveraging electronic health records data to predict multiple sclerosis disease activity.
Yuri AhujaNicole KimLiang LiangTianrun CaiKumar DahalThany SeyokChen LinSean FinanKatherine LiaoGuergana SavovoaTanuja ChitinisTianxi CaiZongqi XiaPublished in: Annals of clinical and translational neurology (2021)
Our novel machine-learning algorithm predicts 1-year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR-based two-stage approach of outcome prediction may have application to neurological disease beyond MS.
Keyphrases
- electronic health record
- multiple sclerosis
- disease activity
- machine learning
- systemic lupus erythematosus
- rheumatoid arthritis
- clinical decision support
- rheumatoid arthritis patients
- mass spectrometry
- ankylosing spondylitis
- ms ms
- adverse drug
- juvenile idiopathic arthritis
- white matter
- deep learning
- artificial intelligence
- neural network