Development and validation of a computable phenotype for adolescent idiopathic scoliosis
The CP had good performance in identifying pediatric patients with AIS. Future refinements to the algorithm should include the use of x-ray parameters or the application of natural language processing to unstructured EHR data to better distinguish AIS cases from other spinal diagnoses. This CP is a fundamental step to facilitate a learning health system environment that can rapidly develop evidence to improve pediatric patient outcomes.
Authors
- Sarah B. Floyd (ORCID: https://orcid.org/0000-0001-7412-8136)
- Donna Oeffinger
- Coleman Hilton (ORCID: https://orcid.org/0000-0001-9070-7680)
- Steven W. Hwang (ORCID: https://orcid.org/0000-0003-0832-3683)
- Christian Lowson
- Jason Woloff
- Ashley Mills
Institutions
- Shriners Hospitals for Children - Tampa (US)
- Shriners Hospitals for Children - Philadelphia (US)
- Shriners Hospitals for Children (US)
- Shriners Hospitals for Children Medical Center - Lexington (US)
- Clemson University (US)
Publication Details
- Journal
- Learning Health Systems
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1002/lrh2.70018
- Primary Topic
- Scoliosis diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00