Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary

Sunho Choi, Hyung Joon Joo, Yoojoong Kim, Jong Ho Kim, Junhee Seok

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background A computerized 12-lead electrocardiogram (ECG) can automatically generate diagnostic statements, which are helpful for clinical purposes. Standardization is required for big data analysis when using ECG data generated by different interpretation algorithms. The common data model (CDM) is a standard schema designed to overcome heterogeneity between medical data. Diagnostic statements usually contain multiple CDM concepts and also include non-essential noise information, which should be removed during CDM conversion. Existing CDM conversion tools have several limitations, such as the requirement for manual validation, inability to extract multiple CDM concepts, and inadequate noise removal. Objectives We aim to develop a fully automated text data conversion algorithm that overcomes limitations of existing tools and manual conversion. Methods We used interpretations printed by 12-lead resting ECG tests from three different vendors: GE Medical Systems, Philips Medical Systems, and Nihon Kohden. For automatic mapping, we first constructed an ontology-lexicon of ECG interpretations. After clinical coding, an optimized tool for converting ECG interpretation to CDM terminology is developed using term-based text processing. Results Using the ontology-lexicon, the cosine similarity-based algorithm and rule-based hierarchical algorithm showed comparable conversion accuracy (97.8 and 99.6%, respectively), while an integrated algorithm based on a heuristic approach, ECG2CDM, demonstrated superior performance (99.9%) for datasets from three major vendors. Conclusion We developed a user-friendly software that runs the ECG2CDM algorithm that is easy to use even if the user is not familiar with CDM or medical terminology. We propose that automated algorithms can be helpful for further big data analysis with an integrated and standardized ECG dataset.

Original languageEnglish
Pages (from-to)880-890
Number of pages11
JournalApplied Clinical Informatics
Issue number4
Publication statusPublished - 2022 Sept 21

Bibliographical note

Publisher Copyright:
© 2022. The Author(s).


  • ECG interpretation
  • OMOP common data model
  • clinical coding
  • electrocardiogram
  • ontology
  • ontology-lexicon

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management


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