Work

Using machine learning to identify pediatric ophthalmologists

Year: 2024

Type: article

Study design: Observational Study

Source: Journal of American Association for Pediatric Ophthalmology and Strabismus

DOI: 10.1016/j.jaapos.2024.104052

Authors: Isdin Oke, Tobias Elze, Joan W. Miller, Alice C. Lorch, Mei-Sing Ong, Ann Chen Wu, David G. Hunter, Suzann Pershing, Leslie Hyman, Julia A. Haller, Aaron Y. Lee, Cecilia S. Lee, Joan W. Miller, Alice C. Lorch, Christina Thomas-Virnig, Alex R. Miranda, Divya Srikumaran, Fasika A. Woreta, Flora Lum

Institutions: Massachusetts Eye and Ear Infirmary, Boston Children's Hospital, Harvard University, Stanford University, Wills Eye Hospital, University of Washington, University of Wisconsin–Milwaukee, Johns Hopkins University, American Academy of Ophthalmology

Language: English

Topic: Medical Coding and Health Information

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Eye Institute

Awards: G2399802450, Social Determinants of Vision Health in Children, Core Grant for Vision Research

OpenAlex ID: W4404504470

FWCI: 0.294

Cites: 9

Cited by: 1

Related to: 10