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This paper is in the following e-collection/theme issue:

Natural Language Processing (982) Research Instruments, Questionnaires, and Tools (800) Electronic Health Records (1403) Methods and Instruments in Medical Informatics (224) Clinical Communication, Electronic Consultation and Telehealth (820) Ophthalmology (83)

Published on in Vol 13 (2025)

This is a member publication of University of Pittsburgh

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69216, first published .
Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study

Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study

Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study

Authors of this article:

Lauren M Wasser1 Author Orcid Image ;   Hai-Wei Liang1 Author Orcid Image ;   Chenyu Li2 Author Orcid Image ;   Julie Cassidy1 Author Orcid Image ;   Pooja Tallapaneni1 Author Orcid Image ;   Hunter Osterhoudt2 Author Orcid Image ;   Yanshan Wang2 Author Orcid Image ;   Andrew M Williams1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Williams A, Liang H, Usmani B, Cassidy J, Lin H. Loss to follow-up and increase in cup-to-disc ratio among glaucoma patients in the IRIS Registry. Canadian Journal of Ophthalmology 2026 View
  2. Rajwal S, Pandey A, Zhang Z, Chen Y, Liu M, Das S, Rogers H, Sarker A, Xiao Y. Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review. Journal of Medical Internet Research 2026;28:e83793 View