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Related Experiment Video

Updated: Nov 16, 2025

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

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Published on: September 19, 2025

Machine learning based predictors for COVID-19 disease severity.

Dhruv Patel¹, Vikram Kher¹, Bhushan Desai²

¹Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.

Scientific Reports

|February 26, 2021

Summary

Machine learning models predict intensive care unit (ICU) and mechanical ventilation needs for COVID-19 patients using clinical, demographic, and blood data. Random Forest models accurately identified high-risk patients, aiding healthcare surge capacity planning.

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Area of Science:

Medical Informatics
Computational Biology
Epidemiology

Background:

Effective surge capacity planning is crucial for healthcare systems during pandemics like COVID-19.
Predicting the need for intensive care and mechanical ventilation is key to resource allocation.

Purpose of the Study:

To develop and evaluate machine learning algorithms for predicting intensive care unit (ICU) admission and mechanical ventilation requirements in COVID-19 patients.
To identify key predictors of severe disease progression using socio-demographic, clinical, and blood panel data.

Main Methods:

Utilized socio-demographic, clinical, and blood panel data from initial patient presentations.
Developed and compared various machine learning algorithms, focusing on the Random Forest classifier.
Identified influential features and assessed the impact of excluding blood panel data on prediction accuracy (AUC).

Main Results:

The Random Forest classifier demonstrated superior performance in predicting ICU need (AUC [Formula: see text]) and mechanical ventilation (AUC [Formula: see text]).
All data categories (socio-demographic, clinical, blood panel) were found to be important predictors.
Excluding blood panel data reduced prediction accuracy by 0.12 AUC, highlighting its value in assessing disease severity.
A reduced set of five quantitative features achieved comparable predictive performance to models using all features.

Conclusions:

Machine learning models, particularly Random Forest, can effectively predict the need for intensive care and mechanical ventilation in COVID-19 patients.
Blood panel data significantly contributes to predicting disease severity.
Simplified predictive models using a minimal set of quantitative features offer a robust and less subjective approach for clinical decision-making and resource management.