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 (123) JMIR Theme Issue: COVID-19 Special Issue (2543) Natural Language Processing (879) Telehealth and Telemonitoring (2030)

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 (7) 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
  6. Ba H, Du H, Cheng C, Zhang Y, Ruan L, Chen Z. Identification of Syndrome Types in Patients With Pancreatic Cancer From Free Text in Electronic Medical Records: Model Development and Validation. JMIR Formative Research 2025;9:e70602 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