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

Clinical Information and Decision Making (1408) Nosocomial Infections (20) Ophthalmology (44) Electronic Health Records (1055) Natural Language Processing (693) Infectious Diseases (non-STD/STI) (1575)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18855, first published .
Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

Authors of this article:

Sally L Baxter1, 2 Author Orcid Image ;   Adam R Klie3 Author Orcid Image ;   Bharanidharan Radha Saseendrakumar4 Author Orcid Image ;   Gordon Y Ye2 Author Orcid Image ;   Michael Hogarth2 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) Metrics

Journals

  1. Chen J, Lin W, Yang S, Chiang M, Hribar M. Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record. Translational Vision Science & Technology 2022;11(11):20 View
  2. Chen J, Baxter S. Applications of natural language processing in ophthalmology: present and future. Frontiers in Medicine 2022;9 View
  3. Tavakoli K, Kalaw F, Bhanvadia S, Hogarth M, Baxter S. Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11. Ophthalmology Science 2023;3(4):100337 View
  4. Stein J, Zhou Y, Andrews C, Kim J, Addis V, Bixler J, Grove N, McMillan B, Munir S, Pershing S, Schultz J, Stagg B, Wang S, Woreta F. Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records. American Journal of Ophthalmology 2024;262:153 View
  5. Mora S, Giacobbe D, Bartalucci C, Viglietti G, Mikulska M, Vena A, Ball L, Robba C, Cappello A, Battaglini D, Brunetti I, Pelosi P, Bassetti M, Giacomini M. Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units. Journal of Biomedical Informatics 2024;156:104667 View
  6. Harrigian K, Tran D, Tang T, Gonzales A, Nagy P, Kharrazi H, Dredze M, Cai C. Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods. Ophthalmology Science 2024;4(6):100578 View
  7. Felfeli T, Huang R, Lee T, Lena E, Basilious A, Lamoureux D, Khalid S. Assessment of predictive value of artificial intelligence for ophthalmic diseases using electronic health records: A systematic review and meta-analysis. JFO Open Ophthalmology 2024;7:100124 View
  8. Chen J, Reddy A, Al-Sharif E, Shoji M, Kalaw F, Eslani M, Lang P, Arya M, Koretz Z, Bolo K, Arnett J, Roginiel A, Do J, Robbins S, Camp A, Scott N, Rudell J, Weinreb R, Baxter S, Granet D. Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think like an Ophthalmologist?. Ophthalmology Science 2025;5(1):100600 View