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Literature DB >> 16779029

Identifying UMLS concepts from ECG Impressions using KnowledgeMap.

Joshua C Denny¹, Anderson Spickard, Randolph A Miller, Jonathan Schildcrout, Dawood Darbar, S Trent Rosenbloom, Josh F Peterson.

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.

Mesh：

Year: 2005 PMID： 16779029 PMCID： PMC1479847

Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076

11 in total

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Journal: J Am Med Inform Assoc Date: 2001 Jan-Feb Impact factor: 4.497

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8. Oral erythromycin and the risk of sudden death from cardiac causes.

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9. The diagnostic performance of computer programs for the interpretation of electrocardiograms.

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Journal: N Engl J Med Date: 1991-12-19 Impact factor: 91.245

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5. Learning to identify treatment relations in clinical text.

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