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BMC Cardiovascular Disorders

, 17:151



BackgroundIn order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study.

ImplementationA natural language processing system using a dictionary lookup, rules, and patterns was developed to extract heart function measurements that are typically recorded in echocardiogram reports as measurement-value pairs. Curated semantic bootstrapping was used to create a custom dictionary that extends existing terminologies based on terms that actually appear in the medical record. A novel disambiguation method based on semantic constraints was created to identify and discard erroneous alternative definitions of the measurement terms. The system was built utilizing a scalable framework, making it available for processing large datasets.

ResultsThe system was developed for and validated on notes from three sources: general clinic notes, echocardiogram reports, and radiology reports. The system achieved F-scores of 0.872, 0.844, and 0.877 with precision of 0.936, 0.982, and 0.969 for each dataset respectively averaged across all extracted values. Left ventricular ejection fraction LVEF is the most frequently extracted measurement. The precision of extraction of the LVEF measure ranged from 0.968 to 1.0 across different document types.

ConclusionsThis system illustrates the feasibility and effectiveness of a large-scale information extraction on clinical data. New clinical questions can be addressed in the domain of heart failure using retrospective clinical data analysis because key heart function measurements can be successfully extracted using natural language processing.

KeywordsNatural language processing Text mining Information extraction Echocardiography Heart function Left ventricular ejection fraction Electronic supplementary materialThe online version of this article doi:10.1186-s12872-017-0580-8 contains supplementary material, which is available to authorized users.

Author: Olga V. Patterson - Matthew S. Freiberg - Melissa Skanderson - Samah J. Fodeh - Cynthia A. Brandt - Scott L. DuVall

Source: https://link.springer.com/


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