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

Surveillance Systems (466) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Infectious Diseases (non-STD/STI) (1592) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Patient-Reported Outcome Measures (PROMs) (294) Natural Language Processing (706) Chronic Conditions (278)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47064, first published .
Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports

Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports

Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports

Authors of this article:

Nick S Menger1 Author Orcid Image ;   Arnaud Tognetti2, 3 Author Orcid Image ;   Michael C Farruggia4 Author Orcid Image ;   Carla Mucignat5 Author Orcid Image ;   Surabhi Bhutani6 Author Orcid Image ;   Keiland W Cooper7 Author Orcid Image ;   Paloma Rohlfs Domínguez8 Author Orcid Image ;   Thomas Heinbockel9 Author Orcid Image ;   Vonnie D C Shields10 Author Orcid Image ;   Anna D'Errico11 Author Orcid Image ;   Veronica Pereda-Loth12 Author Orcid Image ;   Denis Pierron12 Author Orcid Image ;   Sachiko Koyama13 Author Orcid Image ;   Ilja Croijmans14, 15 Author Orcid Image
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