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. 2005:2005:196-200.

Identifying UMLS concepts from ECG Impressions using KnowledgeMap

Joshua C Denny  1 , Anderson Spickard 3rd, Randolph A Miller, Jonathan Schildcrout, Dawood Darbar, S Trent Rosenbloom, Josh F Peterson
Affiliations

Identifying UMLS concepts from ECG Impressions using KnowledgeMap

Joshua C Denny et al. AMIA Annu Symp Proc. 2005.

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.

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Figures

Figure 1
Figure 1
Sample markup of an ECG in the test set. In this example, “diffuse” was entered as an unidentified concept.
See this image and copyright information in PMC

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