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Related Experiment Video

Updated: Apr 16, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Regular expression-based learning to extract bodyweight values from clinical notes.

Maureen A Murtaugh¹, Bryan Smith Gibson¹, Doug Redd²

¹IDEAS Center, Veterans Administration, Salt Lake City Health Care System, Salt Lake City, UT, United States; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States.

Journal of Biomedical Informatics

|March 10, 2015

Summary

This summary is machine-generated.

A new algorithm effectively extracts bodyweight measures from clinical notes, improving data for research. This method captures crucial health information often missing from electronic health records.

Keywords:

Bodyweight Natural language processing Text classification

Related Experiment Videos

Last Updated: Apr 16, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Area of Science:

Health Informatics
Natural Language Processing
Clinical Data Extraction

Background:

Bodyweight measures are vital for clinical care and research but often missing from structured EHR data.
These critical data points are frequently embedded as text within clinical notes.
Existing structured data in electronic health records (EHRs) is insufficient for comprehensive bodyweight-related research.

Purpose of the Study:

To develop and validate a learning algorithm for extracting bodyweight-related measures from clinical notes.
To enhance the completeness of bodyweight data available for clinical research within the Veterans Administration (VA) EHR.
To create a complementary data source for existing structured vital signs data.

Main Methods:

Developed the Regular Expression Discovery Extractor (REDEx), a supervised learning algorithm.
Trained REDEx using annotated clinical notes, establishing an annotation process and identifying relevant terms.
Applied REDEx to a large dataset of clinical notes to extract bodyweight measures and estimate data capture improvements.

Main Results:

REDEx achieved high performance: 98.3% accuracy, 98.8% precision, 98.3% recall, and 98.5% F-score.
7.7% of analyzed notes contained bodyweight measures not present in structured data.
An average of 2 additional bodyweight measures per individual per year were identified.

Conclusions:

Bodyweight measures are frequently documented as unstructured text in clinical notes.
Supervised learning algorithms like REDEx can reliably extract this valuable data.
Extracted data has significant implications for clinical care, epidemiological studies, and quality improvement initiatives.