title: Regular Expression-Based Learning for METs Value Extraction.
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.
authors: VA Salt Lake City Health Care System; Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
authors: Douglas
authors: D
authors: Redd
authors: VA Salt Lake City Health Care System.
authors: Jinqiu
authors: J
authors: Kuang
authors: VA Salt Lake City Health Care System.
authors: April
authors: A
authors: Mohanty
authors: Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
authors: Bruce E
authors: BE
authors: Bray
authors: VA Salt Lake City Health Care System; Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
authors: Qing
authors: Q
authors: Zeng-Treitler