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

Mohamed I Seedahmed 1, 2, MPH, MD;  Izabella Mogilnicka 2, 3, MD;  Siyang Zeng 2, 4, MS;  Gang Luo 4, PhD;  Mary A Whooley 2, 5, 6, MD;  Charles E McCulloch 7, PhD;  Laura Koth 1*, MD;  Mehrdad Arjomandi 1, 2*, MD

1 Division of Pulmonary, Critical Care, Allergy and Immunology, and Sleep, Department of Medicine, University of California San Francisco, San Francisco, CA, US

2 San Francisco Veterans Affairs Medical Center , San Francisco, CA, US

3 Department of Experimental Physiology and Pathophysiology, Laboratory of the Centre for Preclinical Research , Medical University of Warsaw, Warsaw , PL

4 Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington , Seattle, WA, US

5 Department of Medicine, University of California San Francisco , San Francisco, CA, US

6 Measurement Science Quality Enhancement Research Initiative, San Francisco Veterans Affairs Healthcare System , San Francisco, CA, US

7 Department of Epidemiology & Biostatistics, University of California San Francisco , San Francisco, CA, US

*these authors contributed equally

Corresponding Author:

  • Mohamed I Seedahmed, MPH, MD
  • Division of Pulmonary, Critical Care, Allergy and Immunology, and Sleep
  • Department of Medicine
  • University of California San Francisco
  • 513 Parnassus Ave
  • HSE 1314, Box 0111
  • San Francisco, CA
  • US
  • Phone: 1 (415) 476 0735
  • Email: mohamed.seedahmed@ucsf.edu