title: Identifying UMLS concepts from ECG Impressions using KnowledgeMap.
abstract: Electrocardiogram (ECG) impressions represent a wealth of medical information for potential decision support and drug-effect discovery. Much of this information is inaccessible to automated methods in the free-text portion of the ECG report. We studied the application of the KnowledgeMap concept identifier (KMCI) to map Unified Medical Language System (UMLS) concepts from ECG impressions. ECGs were processed by KMCI and the results scored for accuracy by multiple raters. Reviewers also recorded unidentified concepts through the scoring interface. Overall, KMCI correctly identified 1059 out of 1171 concepts for a recall of 0.90. Precision, indicating the proportion of ECG concepts correctly identified, was 0.94. KMCI was particularly effective at identifying ECG rhythms (330/333), perfusion changes (65/66), and noncardiac medical concepts (11/11). In conclusion, KMCI is an effective method for mapping ECG impressions to UMLS concepts.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Anderson
authors.initials: A
authors.lastname: Spickard
authors.forename: Randolph A
authors.initials: RA
authors.lastname: Miller
authors.forename: Jonathan
authors.initials: J
authors.lastname: Schildcrout
authors.forename: Dawood
authors.initials: D
authors.lastname: Darbar
authors.forename: S Trent
authors.initials: ST
authors.lastname: Rosenbloom
authors.forename: Josh F
authors.initials: JF
authors.lastname: Peterson
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D004562:Electrocardiography; D006801:Humans; D008499:Medical Records / Q000145:classification*; D017432:Unified Medical Language System