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

346
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:
346
Classification of Illness01:17

Classification of Illness

8.2K
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...
8.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.4K
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...
8.4K
Prediction Intervals01:03

Prediction Intervals

2.7K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.7K

You might also read

Related Articles

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

Sort by
Same author

Numerical Study on the Mechanical Behavior of Surrounding Rock in Deep Coal Seams under Fluidization Mining.

ACS omega·2026
Same author

Methylation biomarkers for early detection of colorectal cancer: From molecular discovery to clinical translation and application.

Journal of cancer research and therapeutics·2026
Same author

Immune cell engagers in lung cancer.

Frontiers in immunology·2026
Same author

Application of Colloid Material in Prevention and Control of Coal Spontaneous Combustion in Coal Mine.

ACS omega·2026
Same author

Effect of temperature on the wetting performance of coal powder by different surfactant solutions: experimental and molecular dynamics simulation study.

Scientific reports·2025
Same author

lncRNA VIM-AS1 acts as a prognostic biomarker and promotes apoptosis in lung adenocarcinoma.

Journal of Cancer·2023
Same journal

Phylogenomic structure and resistance profiles of clinical Proteus mirabilis from a Thai hospital.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
Same journal

First genetic evidence of Aedes albopictus (Skuse, 1894) (Diptera: Culicidae) establishment and recent invasion in Honduras.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
Same journal

Barcoding gene (cox1 mtDNA) meta-analysis: A continental perspective for Tylodelphys species (Digenea: Diplostomidae).

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
Same journal

Heterogeneity of plasmids containing OXA-48-like and NDM-5 carbapenemases and emergence of OXA-181 and NDM-5 co-carrying strains and plasmids in Escherichia coli from veterinary settings.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
Same journal

Projected late-century climate change alters reproductive gene expression pathways in the arbovirus vector Aedes aegypti.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
Same journal

Diseases of the past were not our diseases: Rethinking retrospective diagnosis in medicine.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2026
See all related articles

Related Experiment Video

Updated: Nov 19, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

182

Machine learning predictive model for severe COVID-19.

Jianhong Kang1, Ting Chen2, Honghe Luo1

  • 1Department of Thoracic Surgery, First Affiliated Hospital, Sun-Yat-sen University, Guangzhou, China.

Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases
|January 30, 2021
PubMed
Summary
This summary is machine-generated.

A new predictive model for severe COVID-19 was developed using artificial neural networks. Key indicators like albumin, globulin, and blood urea nitrogen can help predict disease severity and guide early clinical interventions.

Keywords:
Machine learningPredictive modelSevere COVID-19

More Related Videos

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

1.6K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

660

Related Experiment Videos

Last Updated: Nov 19, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

182
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

1.6K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

660

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Infectious Disease Modeling

Background:

  • Severe COVID-19 poses a significant global health challenge, necessitating accurate predictive tools for patient management.
  • Existing models may not fully capture the complexity of disease progression, highlighting the need for refined prediction strategies.

Purpose of the Study:

  • To develop and evaluate a modified predictive model for severe cases of COVID-19 (caused by SARS-CoV-2).
  • To identify key clinical indicators associated with COVID-19 severity using advanced computational methods.

Main Methods:

  • Clinical data from 151 COVID-19 patients were analyzed.
  • A five-step process including data preprocessing, feature selection, model building (artificial neural networks with TensorFlow), and evaluation was employed.
  • Correlation analysis was used to identify significant predictors of disease severity.

Main Results:

  • The developed artificial neural network model demonstrated strong predictive performance, achieving an area under the curve of 0.953.
  • Albumin (ALB) showed a significant negative correlation with COVID-19 severity, while globulin (GLB) and blood urea nitrogen (BUN) exhibited strong positive correlations.
  • GLB and BUN were identified as potential risk factors for severe COVID-19.

Conclusions:

  • The modified predictive model shows outstanding performance in predicting severe COVID-19.
  • Identifying GLB and BUN as risk factors can aid in early clinical interventions and improve patient outcomes.
  • This model holds significant potential for enhancing the quality of care and cure rates for COVID-19 patients.