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

Formative Evaluation of Digital Health Interventions (2918) Artificial Intelligence (1838) Machine Learning (1825) Ontologies, Classifications, and Coding (282) Surgery, Trauma and Surgical Sciences (178)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67311, first published .
Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study

Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study

Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study

Authors of this article:

Jun Tang1 Author Orcid Image ;   Yang Li2, 3 Author Orcid Image ;   Keyu Luo4 Author Orcid Image ;   Jiangyuan Lai5 Author Orcid Image ;   Xiang Yin4 Author Orcid Image ;   Dongdong Wu1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Tang J, Li T, Liu L, Wu D. Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning. Medical & Biological Engineering & Computing 2025 View