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Updated: Apr 26, 2026

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

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

Published on: September 19, 2025

A systematic review of predictive modeling for bronchiolitis.

Gang Luo¹, Flory L Nkoy², Per H Gesteland²

¹Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT 84108, USA.

International Journal of Medical Informatics

|August 10, 2014

Summary

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Predictive models for childhood bronchiolitis aim to standardize care, but many challenges remain. Further research is needed to develop optimal models for predicting disease course and guiding treatment decisions for this common childhood illness.

Area of Science:

Pediatrics
Medical Informatics
Epidemiology

Background:

Bronchiolitis is a leading cause of hospitalization in young children.
Current management decisions for bronchiolitis are often subjective, leading to practice variations.
Standardizing care requires reliable methods for predicting disease progression.

Purpose of the Study:

To review the current state of predictive modeling for bronchiolitis.
To identify limitations and open problems in existing predictive models.
To stimulate future research for developing optimal predictive models.

Main Methods:

Systematic literature review using PubMed.
Comprehensive search query developed iteratively.
Inclusion criteria: human subjects, English language, children (0-18 years).

Keywords:

Bronchiolitis Machine learning Predictive modeling Respiratory syncytial virus

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

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

Published on: September 19, 2025

Main Results:

Initial search yielded 2312 references.
168 relevant references were identified and discussed.
Identified limitations and open problems in current predictive modeling.

Conclusions:

Significant challenges persist in developing effective predictive models for bronchiolitis.
Future studies must address these limitations to achieve optimal predictive accuracy.
Improved models are crucial for standardizing bronchiolitis management and improving patient outcomes.