Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

medRxiv : the preprint server for health sciences·2026

Same author

Quantitative summarization of high-touch surfaces and epidemiological parameters of <i>Clostridioides difficile</i> acquisition and transmission for mathematical modeling: a systematic review.

Infection control and hospital epidemiology·2025

Same author

Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

PLoS medicine·2024

Same author

Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study.

JMIR infodemiology·2024

Same author

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Nature communications·2023

Same author

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

medRxiv : the preprint server for health sciences·2023

Search research articles

Related Experiment Video

Updated: May 2, 2026

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

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

Published on: July 22, 2025

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity

Haleigh Noelle West-Page¹, Kevin McGoff¹, Harrison Latimer¹

¹Department of Mathematics and Statistics, College of Science, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC, 28223, United States, 1 980-829-8292.

JMIR Formative Research

|April 30, 2026

Summary

Machine learning models accurately predict severe COVID-19 using combined biochemical and clinical data. These models show consistent performance across SARS-CoV-2 variants, identifying key predictors like d-dimer and age.

Keywords:

COVID-19 clinical decision support clinical types data-driven machine learning prediction

Related Experiment Videos

Last Updated: May 2, 2026

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

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

Published on: July 22, 2025

Area of Science:

Biomedical informatics
Machine learning applications in healthcare
Infectious disease modeling

Background:

Accurate differentiation of severe from non-severe COVID-19 is crucial for healthcare system optimization.
Existing methods struggle to classify COVID-19 clinical types, especially with evolving SARS-CoV-2 variants.

Purpose of the Study:

To evaluate the predictability and interpretability of machine learning (ML) techniques for classifying COVID-19 severity.
To assess model performance across different biomedical data types and SARS-CoV-2 variants.

Main Methods:

Utilized comprehensive patient data from 362 (original strain) and 1000 (Omicron variant) patients.
Included 26 biochemical and 26 clinical features for model training and testing.
Applied penalized logistic regression, random forest, k-nearest neighbors, and support vector machines with 50 train-test splits.

Main Results:

The combined (fusion) data modality achieved the highest Area Under the Curve (AUC) of 0.915.
Biochemical and clinical modalities showed AUCs of 0.862 and 0.818, respectively.
Elevated d-dimer, elevated troponin I, and age >55 were top predictors of severe COVID-19.

Conclusions:

Machine learning effectively predicts severe COVID-19 using comprehensive patient data.
Combined biochemical and clinical data enhance predictive performance.
ML models demonstrate potential utility across SARS-CoV-2 variants, warranting further validation.