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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

Pneumonia identification using statistical feature selection.

Cosmin Adrian Bejan¹, Fei Xia, Lucy Vanderwende

¹Department of Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, Washington 98195-7240, USA. bejan@u.washington.edu

Journal of the American Medical Informatics Association : JAMIA

|April 28, 2012

Summary

This summary is machine-generated.

This study developed a natural language processing system to identify pneumonia from clinical notes. Feature selection and assertion values significantly improved pneumonia identification accuracy compared to a baseline system.

Related Experiment Videos

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:

Natural Language Processing
Medical Informatics
Clinical Decision Support

Background:

Pneumonia identification from clinical notes is crucial for patient care.
Automated systems can aid in timely and accurate diagnosis.

Purpose of the Study:

To develop and evaluate a natural language processing (NLP) system for pneumonia identification using patient narrative reports.
To assess the impact of feature selection and assertion values on system performance.

Main Methods:

A binary classifier was trained on clinical notes from 426 intensive care unit patients.
Features included word n-grams, Unified Medical Language System (UMLS) concepts, and assertion values.
A statistical feature selection approach was employed to rank features.

Main Results:

The NLP system achieved F1-measures of 85.71% (restricted dataset) and 81.67% (unrestricted dataset).
These results significantly outperformed a baseline system (50.70% and 49.10% F1-measure, respectively).
Incorporating assertion values further enhanced system performance.

Conclusions:

Statistical feature selection significantly improves NLP system performance for pneumonia identification.
Utilizing assertion values for pneumonia expressions enhances diagnostic accuracy.
The developed NLP system demonstrates a promising approach for automated pneumonia detection in clinical settings.