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

Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Clinical Informatics (1022) Machine Learning (1465) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (573) Artificial Intelligence (1371)

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 (4) Tweetations (2) Metrics

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  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