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

Clinical Information and Decision Making (1915) Electronic Health Records (1319) Machine Learning (2076) Secondary Use of Clinical Data for Research and Surveillance (474) Clinical Informatics (1325) Natural Language Processing (917) Tools, Programs and Algorithms (376) Artificial Intelligence (2129) Big Data (307)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27008, first published .
A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

Authors of this article:

Li-Hung Yao1 Author Orcid Image ;   Ka-Chun Leung1 Author Orcid Image ;   Chu-Lin Tsai2 Author Orcid Image ;   Chien-Hua Huang2 Author Orcid Image ;   Li-Chen Fu1 Author Orcid Image
Article Authors Cited by (29) Tweetations (3) Metrics

Journals

  1. Gatto J, Seegmiller P, Johnston G, Preum S. Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution. JMIR Medical Informatics 2022;10(9):e37770 View
  2. Inokuchi R, Iwagami M, Sun Y, Sakamoto A, Tamiya N. Machine learning models predicting undertriage in telephone triage. Annals of Medicine 2022;54(1):2989 View
  3. Nguyen T, Ho C, Bui H, Ho L, Ta V. Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study. JMIR Formative Research 2023;7:e42895 View
  4. Ragab M, Kateb F, Al-Rabia M, Hamed D, Althaqafi T, AL-Ghamdi A. A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals. International Journal of Environmental Research and Public Health 2023;20(6):4794 View
  5. Çetin S, Cebeci F, Eray O. The effect of computer-based decision support system on emergency department triage: Non-randomised controlled trial. International Emergency Nursing 2023;70:101341 View
  6. Defilippo A, Bertucci G, Zurzolo C, Veltri P, Guzzi P. On the computational approaches for supporting triage systems. Interdisciplinary Medicine 2023;1(3) View
  7. de Koning E, van der Haas Y, Saguna S, Stoop E, Bosch J, Beeres S, Schalij M, Boogers M. AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study. JMIR Cardio 2023;7:e51375 View
  8. Liu P, Zhang J, Liu S, Huo T, He J, Xue M, Fang Y, Wang H, Xie Y, Xie M, Zhang D, Ye Z. Application of artificial intelligence technology in the field of orthopedics: a narrative review. Artificial Intelligence Review 2024;57(1) View
  9. Chae A, Yao M, Sagreiya H, Goldberg A, Chatterjee N, MacLean M, Duda J, Elahi A, Borthakur A, Ritchie M, Rader D, Kahn C, Witschey W, Gee J. Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology. Radiology 2024;310(1) View
  10. Xue Z, Zhang Y, Gan W, Wang H, She G, Zheng X. Quality and Dependability of ChatGPT and DingXiangYuan Forums for Remote Orthopedic Consultations: Comparative Analysis. Journal of Medical Internet Research 2024;26:e50882 View
  11. Ventura C, Denton E, David J. Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review. Medical Devices: Evidence and Research 2024;Volume 17:191 View
  12. Ingielewicz A, Rychlik P, Sieminski M. Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?. Journal of Personalized Medicine 2024;14(6):590 View
  13. Moreno-Sánchez P, Aalto M, van Gils M. Prediction of patient flow in the emergency department using explainable artificial intelligence. DIGITAL HEALTH 2024;10 View
  14. Jeon J, Cho S, Lee D, Lee C, Kim J. BioBridge: Unified Bio-Embedding With Bridging Modality in Code-Switched EMR. IEEE Access 2024;12:141866 View
  15. Elshewey A, Osman A. Orthopedic disease classification based on breadth-first search algorithm. Scientific Reports 2024;14(1) View
  16. Siira E, Johansson H, Nygren J. Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review. Journal of Medical Internet Research 2025;27:e53741 View
  17. Williams E, Brice S, Price D. Mathematical methodology for defining a frequent attender within emergency departments. Frontiers in Disaster and Emergency Medicine 2025;3 View
  18. Adrita S. Systematic Literature Review: The Role of Artificial Intelligence in Emergency Department Decision Making. medtigo Journal of Medicine 2024;1(1):1 View
  19. Brar D, Singh B, Nanda V. An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder. Sustainable Food Technology 2025;3(4):1099 View
  20. Brar D, Singh B, Nanda V. Application of deep learning and explainable artificial intelligence (XAI) for detecting red chilli powder adulteration. Journal of Food Composition and Analysis 2025;146:107947 View
  21. 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
  22. 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
  23. Arnaud É, Moreno-Sanchez P, Elbattah M, Ammirati C, van Gils M, Dequen G, Ghazali D. Development and Clinical Interpretation of an Explainable AI Model for Predicting Patient Pathways in the Emergency Department: A Retrospective Study. Applied Sciences 2025;15(15):8449 View
  24. Hillenmayer A, Lofi B, Langhans S, Elhardt C, Wolf A, Wertheimer C. Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department. British Journal of Ophthalmology 2025:bjo-2025-327824 View
  25. Kim S, Nam S, Lee J. Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement. Journal of Korean Biological Nursing Science 2025;27(3):333 View
  26. Fatai A, Sattayarom C, Laochai W, Faksook E. Effectiveness of AI-assisted ESI triage on accuracy and selected outcomes in emergency nursing: A systematic review. International Emergency Nursing 2025;83:101680 View
  27. Waligora G, Sherwin R, Soucy Z. Can Application of Artificial Intelligence Improve Emergency Department Triage Performance?. The Journal of Emergency Medicine 2025;78:351 View

Books/Policy Documents

  1. Edjinedja K, Barakat O, Desmettre T, Marx T, Elfahim O, Bredy-Maux C. Intelligent Computing. 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