Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

HSR Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents.

Meystre SM, Kim Y, Heavirland J, Williams J, Bray BE, Garvin J. Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents. Studies in health technology and informatics. 2015 Jan 1; 216:609-13.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions


Angiotensin Converting Enzyme Inhibitors (ACEI) and Angiotensin II Receptor Blockers (ARB) are two common medication classes used for heart failure treatment. The ADAHF (Automated Data Acquisition for Heart Failure) project aimed at automatically extracting heart failure treatment performance metrics from clinical narrative documents, and these medications are an important component of the performance metrics. We developed two different systems to detect these medications, rule-based and machine learning-based. The rule-based system used dictionary lookups with fuzzy string searching and showed successful performance even if our corpus contains various misspelled medications. The machine learning-based system uses lexical and morphological features and produced similar results. The best performance was achieved when combining the two methods, reaching 99.3% recall and 98.8% precision. To determine the prescription status of each medication (i.e., active, discontinued, or negative), we implemented a SVM classifier with lexical features and achieved good performance, reaching 95.49% accuracy, in a five-fold cross-validation evaluation.

Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.