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DOI: 10.1055/s-0042-1756427
Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary
Funding This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0360) as well as a grant from the National Research Foundation of Korea (grant number: NRF-2022R1A2C2004003).![](https://www.thieme-connect.de/media/10.1055-s-00035026/202204/lookinside/thumbnails/10-1055-s-0042-1756427_202203ra0078-1.jpg)
Abstract
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.
Keywords
OMOP common data model - ontology - electrocardiogram - clinical coding - ECG interpretation - ontology-lexiconProtection of Human and Animal Subjects
The study protocol was approved by the institutional review board of Korea University Anam Hospital (IRB NO. 2019AN0227). Written informed consent was waived by the institutional review board of Korea University Anam Hospital because of the retrospective study design that posed minimal risk to the participants. The study complied with the principles of the Declaration of Helsinki.
* These authors equally contributed to the study.
Publikationsverlauf
Eingereicht: 04. April 2022
Angenommen: 29. Juli 2022
Artikel online veröffentlicht:
21. September 2022
© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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