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 (119) JMIR Theme Issue: COVID-19 Special Issue (2510) Natural Language Processing (828) Telehealth and Telemonitoring (1929)

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 (6) Tweetations (4) Metrics

Journals

  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
  4. Weidman A, Sedor‐Schiffhauer Z, Zikmund C, Salcido D, Guyette F, Weiss L, Poropatich R, Pinsky M. Words to live by: Using medic impressions to identify the need for prehospital lifesaving interventions. Academic Emergency Medicine 2025;32(5):516 View
  5. Li H, Peng Q, Wang X, Sun W, Li D, Li R. CSTE: A Context-enhanced Speaker-aware Triple Encoding model for intelligent triage and diagnosis in medical dialogue. Information Processing & Management 2025;62(6):104266 View

Conference Proceedings

  1. Li H, Wang X, Du H, Sun W, Peng Q. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). SADE: A Speaker-Aware Dual Encoding Model Based on Diagbert for Medical Triage and Pre-Diagnosis View