BACKGROUND: Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. OBJECTIVES: Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. METHODS: An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. RESULTS: Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. CONCLUSION: The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired. Thieme. All rights reserved.
BACKGROUND: Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. OBJECTIVES: Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. METHODS: An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. RESULTS: Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. CONCLUSION: The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired. Thieme. All rights reserved.
Authors: Joshua C Denny; Anderson Spickard; Randolph A Miller; Jonathan Schildcrout; Dawood Darbar; S Trent Rosenbloom; Josh F Peterson Journal: AMIA Annu Symp Proc Date: 2005
Authors: Jay W Mason; E William Hancock; Leonard S Gettes; James J Bailey; Rory Childers; Barbara J Deal; Mark Josephson; Paul Kligfield; Jan A Kors; Peter Macfarlane; Olle Pahlm; David M Mirvis; Peter Okin; Pentti Rautaharju; Borys Surawicz; Gerard van Herpen; Galen S Wagner; Hein Wellens Journal: J Am Coll Cardiol Date: 2007-03-13 Impact factor: 24.094
Authors: Joshua C Denny; Randolph A Miller; Lemuel Russell Waitman; Mark A Arrieta; Joshua F Peterson Journal: Int J Med Inform Date: 2008-10-19 Impact factor: 4.046
Authors: Curtis B Williams; Jason G Andrade; Nathaniel M Hawkins; Christopher Cheung; Andrew Krahn; Zachary W Laksman; Matthew T Bennett; Brett Heilbron; Shanta Chakrabarti; John A Yeung-Lai-Wah; Marc W Deyell Journal: Heart Date: 2020-07-20 Impact factor: 5.994
Authors: Olga V Patterson; Matthew S Freiberg; Melissa Skanderson; Samah J Fodeh; Cynthia A Brandt; Scott L DuVall Journal: BMC Cardiovasc Disord Date: 2017-06-12 Impact factor: 2.298