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

Foundations of AI (56) Registered Report (733) Artificial Intelligence (1958)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52190, first published .
Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management

Authors of this article:

Dhavalkumar Patel1 Author Orcid Image ;   Prem Timsina1 Author Orcid Image ;   Larisa Gorenstein2 Author Orcid Image ;   Benjamin S Glicksberg3 Author Orcid Image ;   Ganesh Raut1 Author Orcid Image ;   Satya Narayan Cheetirala1 Author Orcid Image ;   Fabio Santana1 Author Orcid Image ;   Jules Tamegue1 Author Orcid Image ;   Arash Kia1 Author Orcid Image ;   Eyal Zimlichman4, 5 Author Orcid Image ;   Matthew A Levin6, 7, 8 Author Orcid Image ;   Robert Freeman1 Author Orcid Image ;   Eyal Klang3 Author Orcid Image
Article Authors Cited by (7) Tweetations Metrics

Journals

  1. Berikol G, Kanbakan A, Ilhan B, Doğanay F. Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education. Turkish Journal of Emergency Medicine 2025;25(2):67 View
  2. Patel D, Klang E, Timsina P. Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management (Preprint). JMIR AI 2025 View
  3. Kovoor J, Carmichael G, Stretton B, Gupta A, Kleinig O, Ittimani M, Fabian J, Tan S, Ng J, Sateakeerthy S, Booth A, Beath A, Kefalianos J, Jacob M, Ahmed S, Chan W, Kovoor P, Gluck S, Gilbert T, Malycha J, Reddi B, Padbury R, Trochsler M, Maddern G, Chew D, Zannettino A, Liew D, Beltrame J, O’Callaghan P, Papendick C, Bacchi S. Emergency Department Patient Journey Prediction with Machine Learning (Preprint). JMIR AI 2024 View
  4. Feng Y, Wu I, Chen T, Lin Z, Chin L, Chang W. Early prediction of hospital admission in the emergency department by generating expanded chief complaints. Intelligent Data Analysis: An International Journal 2025 View
  5. Lai M, Chen I, Lai J, Ge J. Generative AI in hepatology: Transforming multimodal patient-generated data into actionable insights. Hepatology Communications 2025;9(8) View
  6. Masayoshi K, Hashimoto M, Toda N, Mori H, Kobayashi G, Haque H, So M, Jinzaki M. Training Language Models for Estimating Priority Levels in Ultrasound Examination Waitlists: Algorithm Development and Validation. JMIR AI 2025;4:e68020 View

Conference Proceedings

  1. Elbattah M, Arnaud E, Ghazali D, Dequen G. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Exploring the Ethical Challenges of Large Language Models in Emergency Medicine: A Comparative International Review View