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

Applications of AI (104) Artificial Intelligence (1421)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48295, first published .
Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study

Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study

Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study

Authors of this article:

Joshua A Young1 Author Orcid Image ;   Chin-Wen Chang2 Author Orcid Image ;   Charles W Scales3 Author Orcid Image ;   Saurabh V Menon4 Author Orcid Image ;   Chantal E Holy5 Author Orcid Image ;   Caroline Adrienne Blackie6 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Joshua A Young   1 , MD ;   Chin-Wen Chang   2 , PhD ;   Charles W Scales   3 , PhD ;   Saurabh V Menon   4 , BTech ;   Chantal E Holy   5 , MS, PhD ;   Caroline Adrienne Blackie   6 , MS, OD, PhD

1 Department of Ophthalmology, New York University School of Medicine, New York, NY, United States

2 Data Science, Johnson & Johnson MedTech, Raritan, NJ, United States

3 Medical and Scientific Operations, Johnson & Johnson Medtech, Vision, Jacksonville, FL, United States

4 Mu Sigma Business Solutions Private Limited, Bangalore, India

5 Epidemiology and Real-World Data Sciences, Johnson & Johnson MedTech, New Brunswick, NJ, United States

6 Medical and Scientific Operations, Johnson & Johnson MedTech, Vision, Jacksonville, FL, United States

Corresponding Author:

  • Caroline Adrienne Blackie, MS, OD, PhD
  • Medical and Scientific Operations
  • Johnson & Johnson MedTech, Vision
  • 7500 Centurion Parkway
  • Jacksonville, FL, 32256
  • United States
  • Phone: 1 9044331000
  • Email: cblackie@its.jnj.com