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

Public Health Informatics (572) Clinical Information and Decision Making (3863) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (281)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49811, first published .
Man reviews potential adverse event data on multiple computer screens in a modern office.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study

Authors of this article:

Ashley A Holdefer1 Author Orcid Image ;   Jeno Pizarro1 Author Orcid Image ;   Patrick Saunders-Hastings2 Author Orcid Image ;   Jeffrey Beers1 Author Orcid Image ;   Arianna Sang1 Author Orcid Image ;   Aaron Zachary Hettinger3, 4 Author Orcid Image ;   Joseph Blumenthal3 Author Orcid Image ;   Erik Martinez1 Author Orcid Image ;   Lance Daniel Jones1 Author Orcid Image ;   Matthew Deady1 Author Orcid Image ;   Hussein Ezzeldin5 Author Orcid Image ;   Steven A Anderson5 Author Orcid Image
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Journals

  1. Deady M, Duncan R, Sonesen M, Estiandan R, Stimpert K, Cho S, Beers J, Goodness B, Jones L, Forshee R, Anderson S, Ezzeldin H. A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study. Journal of Medical Internet Research 2024;26:e54597 View
  2. Gaspar F, Roberts S, Campion F, Hoeksema L, Kasirsky J, Duncan R, Sonesen M, Estiandan R, Aziz K, Riddler J, Zhou J, Ezzeldin H. LLM and CQL computable phenotypes for myocarditis adverse event feature and outcome determinations using patient FHIR data: a feasibility study. Frontiers in Digital Health 2026;8 View