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CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(04): 880-890
DOI: 10.1055/s-0042-1756427
Research Article

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

Sunho Choi   *
1   School of Electrical Engineering, Korea University, Seoul, South Korea
,
Hyung Joon Joo   *
2   Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, South Korea
3   Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul, South Korea
,
Yoojoong Kim
4   School of Computer Science and Information Engineering, The Catholic University of Korea, Seoul, South Korea
,
Jong-Ho Kim
2   Korea University Research Institute for Medical Bigdata Science, Korea University, Seoul, South Korea
3   Department of Cardiology, Cardiovascular Center, Korea University College of Medicine, Seoul, South Korea
,
Junhee Seok
1   School of Electrical Engineering, Korea University, Seoul, South Korea
› Author Affiliations 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).
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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-lexicon

Protection 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.


Supplementary Material



Publication History

Received: 04 April 2022

Accepted: 29 July 2022

Article published online:
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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