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Related Experiment Video

Updated: May 22, 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

Developing an algorithm to identify people with Chronic Obstructive Pulmonary Disease (COPD) using administrative

Margrethe Smidth¹, Ineta Sokolowski, Lone Kærsvang

¹The Research Unit for General Practice Aarhus, Aarhus University, Aarhus C, Denmark. m.smidth@alm.au.dk

BMC Medical Informatics and Decision Making

|May 24, 2012

Summary

This summary is machine-generated.

A new algorithm using administrative data can effectively identify patients with Chronic Obstructive Pulmonary Disease (COPD). This method aids in proactive care for individuals with chronic lung conditions.

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Last Updated: May 22, 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

Area of Science:

Pulmonary Medicine
Health Informatics
Epidemiology

Background:

Effective identification of individuals with chronic conditions is crucial for proactive care within the Chronic Care Model.
Administrative data offers a potentially simple, inexpensive, and valid method for identifying patient populations needing planned care.

Purpose of the Study:

To develop and validate an algorithm using administrative data to identify patients with Chronic Obstructive Pulmonary Disease (COPD) in a Danish population.
To assess the feasibility of using administrative data for proactive management of chronic respiratory diseases.

Main Methods:

Seven general practices identified patients with known COPD.
An algorithm was developed using administrative data including hospital admissions for lung diagnoses, redeemed lung medication prescriptions, and spirometry results.
Nine algorithms combining 2-4 criteria were tested to optimize positive predictive value and minimize criteria.

Main Results:

The selected algorithm, based on three criteria, achieved a positive predictive value (PPV) of 72.2% in the initial patient cohort (Population A).
In a larger cohort (Population B), the PPV was 65.0%, with a sensitivity of 44.8% after physician confirmation.
Validation in a broader population (Population C) yielded a PPV of 72.9% and a sensitivity of 29.7%.

Conclusions:

An algorithm utilizing administrative data demonstrates sufficient positive predictive value for identifying patients with Chronic Obstructive Pulmonary Disease.
The algorithm identifies a broader group of chronic lung diseases, including asthma, necessitating further refinement.
This tool can support the identification of patients for proactive chronic lung disease management, with ongoing development recommended.