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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

90
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
90
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

40
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
40

You might also read

Related Articles

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

Sort by
Same author

Evolution of intraocular pressure after cataract surgery in nonglaucomatous patients: A post-hoc analysis of PERCEPOLIS clinical trial data.

PloS one·2026
Same author

Diagnostic performance of eosinopenia for identifying bacterial infections in the emergency department: a prospective multicenter study.

BMC emergency medicine·2026
Same author

Central Corneal Thickness 6 Postoperative Months After Pseudophakic-Descemet Membrane Endothelial Keratoplasty, Triple-Descemet Membrane Endothelial Keratoplasty, and Cataract Surgery Alone: A Retrospective Cohort Study and Literature Review.

Cornea·2026
Same author

Migrations and Tuberculosis: A comparative study of Mycobacterium tuberculosis genomic population structure in Brazil and Mozambique to historical triangular slave trade knowledge to reconstruct the origins of tuberculosis infections caused by Lineage 1 in Brazil.

Tuberculosis (Edinburgh, Scotland)·2026
Same author

Ambulatory management of primary spontaneous pneumothorax in the emergency department: EFFI-PNO protocol - a multicentre, cluster-controlled, stepped-wedge, randomised interventional study.

BMJ open·2025
Same author

Preoperative and perioperative factors that predict endothelial cell loss 1 year after uncomplicated Descemet membrane endothelial keratoplasty.

PloS one·2025

Related Experiment Video

Updated: Jun 3, 2025

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

7.0K

Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective

Cyrielle Brossard1,2, Christophe Goetz3, Pierre Catoire4

  • 1Emergency department, CHR Metz-Thionville, Metz, 57000, France.

BMC Emergency Medicine
|January 6, 2025
PubMed
Summary

Predicting patient admissions to emergency departments (ED) can help manage overcrowding. This study developed an AI tool using machine learning, achieving accurate predictions to optimize healthcare resources.

Keywords:
Artificial intelligenceEmergency departmentOvercrowding

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

8.2K

Related Experiment Videos

Last Updated: Jun 3, 2025

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

7.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

8.2K

Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning Applications
  • Public Health Informatics

Background:

  • Emergency department (ED) overcrowding is a significant public health concern.
  • Overcrowding leads to increased healthcare professional workload and diminished patient outcomes.
  • Predictive modeling for ED admissions is crucial for resource management.

Purpose of the Study:

  • To develop and validate an artificial intelligence-based prediction tool for emergency department admissions.
  • To assess the efficacy of machine learning algorithms in forecasting patient influx.

Main Methods:

  • Retrospective, multicenter study conducted in two French EDs from 2010-2019.
  • Collection and analysis of patient arrival and departure times.
  • Comparison of various machine learning algorithms, including XGBoost, for predictive modeling.

Main Results:

  • Developed two predictive models, one for each hospital location.
  • XGBoost algorithm with hyperparameter tuning demonstrated superior performance.
  • Achieved a mean absolute error of 2.63 for Hospital 1 and 2.64 for Hospital 2, indicating successful prediction accuracy.

Conclusions:

  • Successfully constructed and validated a robust tool for predicting ED admissions.
  • The developed prediction tool can aid in optimizing healthcare professional resource allocation within EDs.
  • Integration of such AI tools is recommended for improved ED operational efficiency.