• Roy J Byrd, Steven R Steinhubl, Jimeng Sun, Shahram Ebadollahi, and Walter F Stewart.
• IBM T. J. Watson Research Center, Yorktown Heights, NY, United States. Electronic address: roybyrd@us.ibm.com.
• Int J Med Inform. 2014 Dec 1;83(12):983-92.

ObjectiveEarly detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.DesignWe developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown.MeasurementsPrecision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling.ResultsOur criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932.ConclusionOur system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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