Related Experiment Video
Updated: Aug 6, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language
Qing T Zeng1, Sergey Goryachev, Scott Weiss
1Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA. qzeng@dsg.harvard.edu
Background:
The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.
Methods:
The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.
Results:
The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.
Conclusion:
We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
Related Concept Videos
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History
Asthma-IV: Diagnostic and Management
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
Chronic Obstructive Pulmonary Disease III: Chronic Bronchitis Features
COPD: Management Using Bronchodilators and Corticosteroids
Asthma-III: Symptoms and Complications
Classification of Asthma
Asthma-IV: Nursing Management
First, in...