Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

779
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
779
Traumatic Brain Injury l: Introduction01:28

Traumatic Brain Injury l: Introduction

25
DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...
25

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fusion of X-Ray Images and Clinical Data for a Multimodal Deep Learning Prediction Model of Osteoporosis: Algorithm Development and Validation Study.

JMIR medical informatics·2025
Same author

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Journal of medical Internet research·2024
Same author

Human apo-SRP72 and SRP68/72 complex structures reveal the molecular basis of protein translocation.

Journal of molecular cell biology·2017
Same author

Dickkopf-Related Protein 2 is Epigenetically Inactivated and Suppresses Colorectal Cancer Growth and Tumor Metastasis by Antagonizing Wnt/β-Catenin Signaling.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2017
Same author

A Novel Technique for Generating and Observing Chemiluminescence in a Biological Setting.

Journal of visualized experiments : JoVE·2017
Same author

A Novel Arch-Shape Nanogenerator Based on Piezoelectric and Triboelectric Mechanism for Mechanical Energy Harvesting.

Nanomaterials (Basel, Switzerland)·2017

Related Experiment Video

Updated: May 2, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K

Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language

Jun Tang1, Tao Li2, Liangming Liu3

  • 1Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China.

Medical & Biological Engineering & Computing
|July 11, 2025
PubMed
Summary

This study introduces an AI model using natural language processing (NLP) and machine learning (ML) for rapid trauma injury classification in emergencies. The AI model significantly improves prediction accuracy for faster, more efficient emergency medical treatment.

Keywords:
Injury severity scoreMachine learningNatural language processingTiered medical treatmentTrauma

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.2K

Related Experiment Videos

Last Updated: May 2, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.2K

Area of Science:

  • Emergency Medicine
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Trauma is a leading cause of global morbidity and mortality.
  • Effective emergency response requires rapid injury classification for resource allocation and treatment prioritization.
  • Chaotic emergency scenes hinder timely and accurate data collection.

Purpose of the Study:

  • To develop a fast, tiered medical treatment model for trauma patients under limited data conditions.
  • To integrate natural language processing (NLP) and machine learning (ML) for improved emergency rescue operations.
  • To enhance the accuracy and efficiency of injury classification in critical situations.

Main Methods:

  • Utilized a dataset of 26,810 trauma patients from Chongqing Daping Hospital (2013-2024).
  • Developed a two-layer model combining NLP for unstructured text and four ML algorithms for structured data.
  • Performed external validation on 245 cases from the Chongqing Emergency Center.

Main Results:

  • The proposed NLP and ML model achieved 91.17% accuracy on the test dataset, outperforming the MLP model by 4.33%.
  • Achieved high performance metrics: 97.06% specificity, 86.85% F1-score, and 0.949 AUC.
  • External validation demonstrated strong generalizability with 87.35% accuracy, 95.78% specificity, 80.37% F1-score, and 0.848 AUC.

Conclusions:

  • The integrated NLP and ML model enables rapid tiered medical treatment using limited emergency data.
  • The model demonstrates significant advantages in prediction accuracy and generalizability for emergency trauma care.
  • AI-driven approaches can transform emergency rescue models, improving efficiency and patient outcomes.