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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

173
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:
173

You might also read

Related Articles

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

Sort by
Same author

IRF8 and IRF3 cooperatively regulate rapid interferon-β induction in human blood monocytes.

Blood·2011
Same author

Reduced order modeling of passive and quasi-active dendrites for nervous system simulation.

Journal of computational neuroscience·2011
Same author

Controlled synthesis and self-assembly of highly monodisperse Ag and Ag(2)S nanocrystals.

Chemistry (Weinheim an der Bergstrasse, Germany)·2011
Same author

Comparison of inlet geometry in microfluidic cell affinity chromatography.

Analytical chemistry·2011
Same author

Ion-exchange synthesis of a micro/mesoporous Zn2GeO4 photocatalyst at room temperature for photoreduction of CO2.

Chemical communications (Cambridge, England)·2011
Same author

A microarray-based approach identifies ADP ribosylation factor-like protein 2 as a target of microRNA-16.

The Journal of biological chemistry·2011

Related Experiment Video

Updated: Aug 30, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

491

Developing and Validating an Emergency Triage Model Using Machine Learning Algorithms with Medical Big Data.

ZhenZhen Gao1, Xuan Qi1, XingTing Zhang2

  • 1Department of Emergency, Beijing Chao-yang Hospital, Capital Medical University, Beijing, 100008, People's Republic of China.

Risk Management and Healthcare Policy
|August 26, 2022
PubMed
Summary

This study developed an emergency triage model using big data analysis and the Extreme Gradient Boosting (XGBoost) algorithm. The model achieved 82.57% accuracy, improving emergency department efficiency and reducing clinician workload.

Keywords:
XGBoost modelemergencytriagetriage model

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Aug 30, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

491
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Emergency Medicine
  • Data Science
  • Health Informatics

Background:

  • Emergency departments (EDs) face challenges in accurately and efficiently triaging patients.
  • Traditional triage methods can be subjective and time-consuming, impacting patient flow and outcomes.
  • The increasing volume of patient data presents an opportunity for data-driven improvements in triage.

Purpose of the Study:

  • To develop and validate an emergency triage prediction model using big data analytics.
  • To enhance the accuracy and efficiency of patient triage in the emergency department.
  • To reduce the workload and improve the working efficiency of medical staff.

Main Methods:

  • Statistical analysis of a large dataset (276,164 patients) from a hospital information system (2017-2020).
  • Development of an Extreme Gradient Boosting (XGBoost) model to predict patient triage levels (I-IV).
  • Model validation using an 80/20 training/testing split, with performance assessed by accuracy and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves.

Main Results:

  • The XGBoost model achieved a prediction accuracy of 82.57%.
  • High AUC values were recorded for each triage level: Level I (0.9629), Level II (0.9554), Level III (0.9120), and Level IV (0.9296).
  • The model's performance was statistically compared against other models using De Long's test.

Conclusions:

  • The developed emergency triage prediction model demonstrates strong accuracy.
  • Implementation of this model can potentially decrease the workload for healthcare professionals.
  • The model offers a promising approach to improve the overall efficiency of emergency department triage.