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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Theme Issue: Medical Informatics and COVID-19 (104) JMIR Theme Issue: COVID-19 Special Issue (2394) Natural Language Processing (650) Telehealth and Telemonitoring (1575)

Published on in Vol 10, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37770, first published .
Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution

Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution

Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution

Authors of this article:

Joseph Gatto1 Author Orcid Image ;   Parker Seegmiller1 Author Orcid Image ;   Garrett Johnston1 Author Orcid Image ;   Sarah Masud Preum1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Joseph Gatto   1 , BA ;   Parker Seegmiller   1 , BSc ;   Garrett Johnston   1 , BA ;   Sarah Masud Preum   1 , BSc, MSc, PhD

1 Department of Computer Science, Dartmouth College, Hanover, NH, United States

Corresponding Author: