Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing
Abstract
Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 22, 2022
- Source ID
- 10.1093/jamia/ocac018
Entities
People
- Adam S Greenblatt
- Akash R Pattnaik
- Alana Kornspun
- Brian Litt
- Brittany H. Scheid
- Catherine V Kulick-soper
- Chadric O Garrick
- Colin A Ellis
- Dan Roth
- Danmeng Wei
- Erin C. Conrad
- Jal M Panchal
- John M Bernabei
- Joongwon Kim
- Kevin Xie
- Micah Weitzman
- Nina J Ghosn
- Peter D Galer
- Ramya Muthukrishnan
- Ryan S. Gallagher
- Steven N Baldassano
- Tara Jennings
Organizations
- American Academy of Neurology
- Children's Hospital of Philadelphia
- National Institute of Neurological Disorders and Stroke
- Office of Naval Research
- University of Pennsylvania