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

You might also read

Related Articles

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

Sort by
Same author

Compact neural network algorithm for electrocardiogram classification.

Physical and engineering sciences in medicine·2026
Same author

High-density three-dimensional image reconstruction using rapid modulation of light.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same author

Quantitative Analysis of Arsenic- and Sucrose-Induced Liver Collagen Remodeling Using Machine Learning on Second-Harmonic Generation Microscopy Images.

Cells·2026
Same author

Smart Machine Vision for Universal Spatial-Mode Reconstruction.

IEEE transactions on neural networks and learning systems·2025
Same author

Improving the coverage area and flake size of ReS<sub>2</sub>through machine learning in APCVD.

Nanotechnology·2024
Same author

Modeling the Electrical Activity of the Heart via Transfer Functions and Genetic Algorithms.

Biomimetics (Basel, Switzerland)·2024

Related Experiment Video

Updated: Oct 19, 2025

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

7.7K

Identification of high-risk COVID-19 patients using machine learning.

Mario A Quiroz-Juárez1, Armando Torres-Gómez2, Irma Hoyo-Ulloa2

  • 1Departamento de Física, Universidad Autónoma Metropolitana Unidad Iztapalapa, Ciudad de México, México.

Plos One
|September 20, 2021
PubMed
Summary

This study introduces a machine-learning algorithm to predict COVID-19 patient survival. The model accurately identifies high-risk individuals, aiding in healthcare planning and treatment prioritization during the pandemic.

More Related Videos

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

8.4K
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

7.3K

Related Experiment Videos

Last Updated: Oct 19, 2025

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

7.7K
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

8.4K
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

7.3K

Area of Science:

  • Computational biology
  • Epidemiology
  • Medical informatics

Background:

  • The COVID-19 pandemic caused by SARS-CoV-2 has led to significant global mortality and economic impact.
  • Accurate prediction of patient outcomes is crucial for effective resource allocation and treatment strategies.

Purpose of the Study:

  • To develop and validate a machine-learning algorithm for predicting COVID-19 patient survival.
  • To assist healthcare professionals in identifying high-risk patients for timely intervention and improved hospital capacity planning.

Main Methods:

  • A machine-learning algorithm was trained using historical data from confirmed and suspected COVID-19 cases in Mexico.
  • The dataset included medical history, demographic information, and COVID-19 specific data.
  • The algorithm was evaluated for its accuracy in predicting survival versus mortality across different clinical stages.

Main Results:

  • The machine-learning algorithm demonstrated high accuracy in identifying high-risk COVID-19 patients.
  • The method proved effective across four distinct clinical stages of the disease.
  • The algorithm's predictive capabilities can enhance hospital capacity planning and facilitate prompt medical treatment.

Conclusions:

  • The developed machine-learning tool offers a valuable resource for medical professionals in assessing COVID-19 patient prognosis.
  • The algorithm can support real-time decision-making for prioritizing healthcare needs during the pandemic.
  • The methodology shows potential for application in statistical hypothesis testing within biological and medical research.