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

119
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:
119
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

529
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
529

You might also read

Related Articles

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

Sort by
Same author

Association between TNFa rs1800629 and migraine: Case-control study and updated meta-analysis.

AIMS neuroscience·2026
Same author

Impact of vitamin D receptor gene variants on psoriasis vulgaris susceptibility and clinical phenotype in a Greek population.

Experimental and therapeutic medicine·2026
Same author

In-Treatment Kinetics of Peripheral Blood Immune Markers in PD-L1 High Non-Small Cell Lung Cancer and Prognostic Relevance for Immunotherapy Outcomes.

Cancers·2026
Same author

Immunohistochemical Expression of IDO and PD-L1 in Distinct Compartments of Breast Cancer Tissue: Correlation with Clinicopathological Features and Outcomes.

Cancers·2026
Same author

Clinically confirmed cohort reveals antioxidant genetic polymorphisms as potential susceptibility factors for long COVID after mild or asymptomatic COVID-19.

Free radical biology & medicine·2026
Same author

Serum-Soluble Receptor for Advanced Glycation End Products as a Potential Biomarker in Lung Cancer Patients.

Journal of personalized medicine·2026

Related Experiment Video

Updated: Jun 19, 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

7.1K

A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19.

Avgi Christodoulou1,2, Martha-Spyridoula Katsarou1, Christina Emmanouil3,4,5

  • 1Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece.

Biotech (Basel (Switzerland))
|July 25, 2024
PubMed
Summary

Machine learning identified key factors like age, sex, hypertension, obesity, and cancer linked to severe COVID-19 outcomes in unvaccinated patients. This aids personalized treatment and encourages vaccination for vulnerable groups.

Keywords:
SARS-CoV-2agecancerhypertensionmachine learningobesitysevere COVID-19sex

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
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.6K

Related Experiment Videos

Last Updated: Jun 19, 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

7.1K
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
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.6K

Area of Science:

  • Medical Informatics
  • Public Health
  • Epidemiology

Background:

  • The COVID-19 pandemic highlighted significant variations in patient outcomes.
  • Predictive modeling offers a method to understand these outcome disparities.
  • Identifying risk factors is crucial for effective disease management.

Purpose of the Study:

  • To correlate demographic and clinical patient data with COVID-19 severity.
  • To demonstrate the utility of machine learning (ML) in predicting COVID-19 prognosis.
  • To develop a web tool for predicting disease outcomes.

Main Methods:

  • Enrolled 344 unvaccinated patients with confirmed SARS-CoV-2 infection.
  • Integrated data from questionnaires and medical records.
  • Applied various classification machine learning algorithms to identify predictive features.

Main Results:

  • Identified age, sex, hypertension, obesity, and cancer as significant predictors of severe COVID-19.
  • Selected the optimal machine learning algorithm and hyperparameters for prediction.
  • Developed a prognostic tool based on 111 independent features.

Conclusions:

  • The developed prognostic tool can guide personalized therapeutic strategies for COVID-19 patients.
  • The tool may encourage vaccination among vulnerable populations by illustrating potential risks.
  • Machine learning is a valuable approach for disease prognosis and personalized medicine.