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

Clinical Information and Decision Making (1328) Electronic Health Records (966) Machine Learning (1384) Secondary Use of Clinical Data for Research and Surveillance (320) Clinical Informatics (912) Natural Language Processing (644) Tools, Programs and Algorithms (243) Artificial Intelligence (1197) Big Data (219)

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 (14) 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

Books/Policy Documents

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