Regular Expression-Based Learning for METs Value Extraction.

Redd D, Kuang J, Mohanty A, Bray BE, Zeng-Treitler Q. Regular Expression-Based Learning for METs Value Extraction. AMIA Summits on Translational Science proceedings. 2016 Jul 20; 2016:213-20.

Related HSR&D Project(s)

• CRE 12-315 – A VHA NLP Software Ecosystem for Collaborative Development and Integration

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Abstract:

Functional status as measured by exercise capacity is an important clinical variable in the care of patients with cardiovascular diseases. Exercise capacity is commonly reported in terms of Metabolic Equivalents (METs). In the medical records, METs can often be found in a variety of clinical notes. To extract METs values, we adapted a machine-learning algorithm called REDEx to automatically generate regular expressions. Trained and tested on a set of 2701 manually annotated text snippets (i.e. short pieces of text), the regular expressions were able to achieve good accuracy and F-measure of 0.89 and 0.86. This extraction tool will allow us to process the notes of millions of cardiovascular patients and extract METs value for use by researchers and clinicians.