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

Formative Evaluation of Digital Health Interventions (2226) Electronic Health Records (971) Pilot studies (ehealth) (178) Clinical Informatics (919) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147)

Published on in Vol 6, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31615, first published .
Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers

Authors of this article:

Mohamed I Seedahmed1, 2 Author Orcid Image ;   Izabella Mogilnicka2, 3 Author Orcid Image ;   Siyang Zeng2, 4 Author Orcid Image ;   Gang Luo4 Author Orcid Image ;   Mary A Whooley2, 5, 6 Author Orcid Image ;   Charles E McCulloch7 Author Orcid Image ;   Laura Koth1 Author Orcid Image ;   Mehrdad Arjomandi1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (5) Metrics

Journals

  1. Seedahmed M, Baugh A, Albirair M, Luo Y, Chen J, McCulloch C, Whooley M, Koth L, Arjomandi M. Epidemiology of Sarcoidosis in U.S. Veterans from 2003 to 2019. Annals of the American Thoracic Society 2023;20(6):797 View
  2. Xia E, Gaurav A, Noe M, Mostaghimi A, Imadojemu S. Validation of the Diagnostic Code for Cutaneous Sarcoidosis in an Electronic Health Database: A Cross-Sectional Analysis. Journal of Investigative Dermatology 2024 View
  3. Rivera N. Big data in sarcoidosis. Current Opinion in Pulmonary Medicine 2024 View