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

Clinical Information and Decision Making (1600) Decision Support for Health Professionals (1321) Clinical Informatics (1127) Machine Learning (1680) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (627) Artificial Intelligence (1660)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49283, first published .
An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

Authors of this article:

Seungseok Lee1 Author Orcid Image ;   Wu Seong Kang2 Author Orcid Image ;   Do Wan Kim3 Author Orcid Image ;   Sang Hyun Seo4 Author Orcid Image ;   Joongsuck Kim2 Author Orcid Image ;   Soon Tak Jeong5 Author Orcid Image ;   Dong Keon Yon6, 7 Author Orcid Image ;   Jinseok Lee1 Author Orcid Image
Article Authors Cited by (7) Tweetations (2) Metrics

Journals

  1. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
  2. 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
  3. Zhang J, Jin Z, Tang B, Huang X, Wang Z, Chen Q, He J. Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data. Bioengineering 2024;11(8):768 View
  4. Kim Y, Yang S, Lee K. Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation. Applied Sciences 2024;14(16):6876 View
  5. Porto B. Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review. BMC Emergency Medicine 2024;24(1) View
  6. Hong T, Huang J, Deng J, Kuang L, Sun M, Wang Q, Luo C, Zhao J, Liu X, Wang H. The Scoring Model to Predict ICU Stay and Mortality After Emergency Admissions in Atrial Fibrillation: A Retrospective Study of 30 366 Patients. Clinical Cardiology 2025;48(2) View
  7. Deleon A, Murala A, Decker I, Rajasekaran K, Moreira A. Machine learning-based prediction of mortality in pediatric trauma patients. Frontiers in Pediatrics 2025;13 View