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

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

You might also read

Related Articles

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

Sort by

Same author

Structures of CRP antigen-antibody complexes provide insights into the mechanism of specific recognition.

Communications biology·2025

Same author

GlmS plays a key role in the virulence factor expression and biofilm formation ability of <i>Staphylococcus aureus</i> promoted by advanced glycation end products.

Virulence·2024

Same author

Sestrin2 Overexpression Ameliorates Endoplasmic Reticulum Stress-Induced Apoptosis via Inhibiting mTOR Pathway in HepG2 Cells.

International journal of endocrinology·2022

Same author

Hypoxia-autophagy axis induces VEGFA by peritoneal mesothelial cells to promote gastric cancer peritoneal metastasis through an integrin α5-fibronectin pathway.

Journal of experimental & clinical cancer research : CR·2020

Same author

Rapid electrochemical reduction of a typical chlorinated organophosphorus flame retardant on copper foam: degradation kinetics and mechanisms.

Chemosphere·2020

Same author

The NAD-dependent deacetylase SIRT2 regulates T cell differentiation involved in tumor immune response.

International journal of biological sciences·2020

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

Separation of Immune Cell Subpopulations in Peripheral Blood Samples from Children with Infectious Mononucleosis

Separation of Immune Cell Subpopulations in Peripheral Blood Samples from Children with Infectious Mononucleosis

Published on: September 7, 2022

Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective

Wenshen Gu^1,2, Shoufu He^1,2, Xiaohui Li³

¹College of Medical Technology, Guangdong Medical University, Dongguan, Guangdong, P.R. China.

Journal of Medical Virology

|August 1, 2025

Summary

This study developed cost-effective machine learning models to diagnose infectious mononucleosis (IM) in children, outperforming traditional methods. These models use clinical features and inflammatory markers, aiding diagnosis in settings without specialized tests.

Keywords:

IM SHAP diagnostic model machine learning serum inflammatory indices

More Related Videos

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection

Published on: December 10, 2013

An Efficient and Simple Method to Establish NK and T Cell Lines from Patients with Chronic Active Epstein-Barr Virus Infection

An Efficient and Simple Method to Establish NK and T Cell Lines from Patients with Chronic Active Epstein-Barr Virus Infection

Published on: March 30, 2018

Related Experiment Videos

Last Updated: Sep 13, 2025

Separation of Immune Cell Subpopulations in Peripheral Blood Samples from Children with Infectious Mononucleosis

Separation of Immune Cell Subpopulations in Peripheral Blood Samples from Children with Infectious Mononucleosis

Published on: September 7, 2022

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection

Published on: December 10, 2013

An Efficient and Simple Method to Establish NK and T Cell Lines from Patients with Chronic Active Epstein-Barr Virus Infection

An Efficient and Simple Method to Establish NK and T Cell Lines from Patients with Chronic Active Epstein-Barr Virus Infection

Published on: March 30, 2018

Area of Science:

Pediatrics
Infectious Diseases
Machine Learning in Medicine

Background:

Infectious mononucleosis (IM) and acute respiratory tract infections (ARTI) share similar clinical symptoms in children.
Accurate and timely diagnosis of IM is crucial but can be challenging due to overlapping presentations.
Existing diagnostic methods may be costly or inaccessible in certain healthcare settings.

Purpose of the Study:

To develop and validate cost-efficient diagnostic models for IM in children using machine learning.
To leverage the Shapley Additive explanation (SHAP) algorithm for model interpretability and feature selection.
To compare the performance of novel diagnostic models against traditional indicators and EBV-specific tests.

Main Methods:

Retrospective analysis of 853 pediatric patients diagnosed with IM across three medical centers.
Application of four machine learning techniques (GBM, XGBoost, RSF identified as best performers) using 49 clinical features and serum inflammatory markers.
Model evaluation via ROC curve analysis and interpretation using SHAP to identify key predictive features (Lymphocyte, PLR, LDH, SII, Age).

Main Results:

The developed models achieved diagnostic performance comparable to EBV-specific tests.
The five-indicator models demonstrated higher diagnostic value than atypical lymphocytes and EBV-DNA load.
Models proved effective across different pediatric age groups and are independent of EBV-specific test results.

Conclusions:

Novel machine learning models offer a superior and cost-efficient diagnostic tool for differentiating IM from ARTI in children.
These models are particularly valuable for primary healthcare units and institutions lacking EBV-specific diagnostic capabilities.
The study provides a promising alternative for accurate IM diagnosis, improving patient management and reducing healthcare costs.