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 (107) JMIR Theme Issue: COVID-19 Special Issue (2461) Natural Language Processing (706) Telehealth and Telemonitoring (1713)

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 (4) Tweetations (4) Metrics

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  1. Sangariyavanich E, Ponthongmak W, Tansawet A, Theera-Ampornpunt N, Numthavaj P, McKay G, Attia J, Thakkinstian A. Systematic review of natural language processing for recurrent cancer detection from electronic medical records. Informatics in Medicine Unlocked 2023;41:101326 View
  2. Raff D, Stewart K, Yang M, Shang J, Cressman S, Tam R, Wong J, Tammemägi M, Ho K. Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning–Enhanced Approaches. Interactive Journal of Medical Research 2024;13:e56729 View
  3. Romero J, Feijoo-Garcia M, Nanda G, Newell B, Magana A. Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis. Big Data and Cognitive Computing 2024;8(10):132 View