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

133
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:
133

You might also read

Related Articles

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

Sort by
Same author

Crossover phenomena of percolation transition in evolution networks with hybrid attachment.

Chaos (Woodbury, N.Y.)·2016
Same author

Relation Between C-X-C Motif Chemokine Receptor 4 Levels and the Presence and Extent of Angiographic Coronary Collaterals in Patients With Chronic Total Coronary Occlusion.

The American journal of cardiology·2016
Same author

Quantify patient-specific coronary material property and its impact on stress/strain calculations using in vivo IVUS data and 3D FSI models: a pilot study.

Biomechanics and modeling in mechanobiology·2016
Same author

Analysis of Genomewide DNA Methylation Reveals Differences in DNA Methylation Levels between Dormant and Naturally as well as Artificially Potentiated Pedicle Periosteum of Sika Deer (Cervus nippon).

Journal of experimental zoology. Part B, Molecular and developmental evolution·2016
Same author

Value of corneal epithelial and Bowman's layer vertical thickness profiles generated by UHR-OCT for sub-clinical keratoconus diagnosis.

Scientific reports·2016
Same author

MAPK-Mediated YAP Activation Controls Mechanical-Tension-Induced Pulmonary Alveolar Regeneration.

Cell reports·2016

Related Experiment Video

Updated: Jul 9, 2025

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B
05:38

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Published on: January 30, 2017

15.8K

Machine-learning-algorithms-based diagnostic model for influenza A in children.

Qian Zeng1,2, Chun Yang1,2, Yurong Li1,2

  • 1Clinical Laboratory, Children's Hospital Affiliated to Shandong University, Jinan, China.

Medicine
|December 5, 2023
PubMed
Summary

This study developed a computer algorithm model using blood test data to quickly and accurately diagnose influenza A in children. The model offers a simpler, more accessible alternative to traditional nucleic acid testing for early-stage diagnosis.

More Related Videos

High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.3K
Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
09:07

Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses

Published on: January 20, 2017

9.9K

Related Experiment Videos

Last Updated: Jul 9, 2025

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B
05:38

Rapid Molecular Detection and Differentiation of Influenza Viruses A and B

Published on: January 30, 2017

15.8K
High-throughput Detection Method for Influenza Virus
10:05

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

26.3K
Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses
09:07

Using Zebrafish Models of Human Influenza A Virus Infections to Screen Antiviral Drugs and Characterize Host Immune Cell Responses

Published on: January 20, 2017

9.9K

Area of Science:

  • Pediatric Infectious Diseases
  • Computational Diagnostics
  • Biomarker Discovery

Background:

  • Nucleic acid testing, the current gold standard for influenza A diagnosis, is costly, time-consuming, and not widely accessible in primary healthcare settings.
  • There is a need for a rapid, accurate, and simple diagnostic method for influenza A, especially for early detection in children.

Purpose of the Study:

  • To establish a diagnostic model for accurately distinguishing influenza A from influenza-like illnesses in children.
  • To develop a model utilizing readily available blood routine test data for early influenza A diagnosis.

Main Methods:

  • Recruited 4188 children with influenza-like symptoms between December 2019 and August 2023.
  • Employed machine learning algorithms including random forest, Gradient Boosting Decision Tree (GBDT), XGBoost, and logistic regression (LR) for model development.
  • Evaluated model performance using validation datasets, focusing on AUC, sensitivity, and specificity.

Main Results:

  • The GBDT model demonstrated the highest performance with an AUC of 0.872, sensitivity of 77.23%, and specificity of 80.29%.
  • Key features identified for diagnosis included lymphocyte (LYM) count, age, serum amyloid A (SAA), white blood cells (WBC) count, and platelet-to-lymphocyte ratio (PLR).
  • All developed models showed promising diagnostic capabilities, with AUC values ranging from 0.784 to 0.872.

Conclusions:

  • A diagnostic model based on blood routine test data was successfully established for identifying influenza A in children.
  • This computer algorithm-based model provides an accurate and accessible tool for early-stage diagnosis of influenza A.
  • The model's simplicity and reliance on routine blood tests make it suitable for widespread adoption in grassroots hospitals.