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

Formative Evaluation of Digital Health Interventions (1855) Electronic Health Records (869) Pilot studies (ehealth) (122) Clinical Informatics (706) Clinical Information and Decision Making (1121) Decision Support for Health Professionals (926)

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 Seedahmed 1, 2 Author Orcid Image ;   Izabella Mogilnicka 2, 3 Author Orcid Image ;   Siyang Zeng 2, 4 Author Orcid Image ;   Gang Luo 4 Author Orcid Image ;   Mary A Whooley 2, 5, 6 Author Orcid Image ;   Charles E McCulloch 7 Author Orcid Image ;   Laura Koth 1 Author Orcid Image ;   Mehrdad Arjomandi 1, 2 Author Orcid Image
Article Authors Cited by (1) 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