Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room
The developed machine learning model significantly enhanced AHF detection in the ER using conventional 12-lead ECGs combined with clinical data. These findings suggest that ECGs, a common tool in the ER, can effectively help screen for AHF.
Authors
- Jung-Joon Cha
- Cheol Woong Yu (ORCID: https://orcid.org/0000-0002-5871-4562)
- Hyung Joon Joo (ORCID: https://orcid.org/0000-0003-1846-8464)
- Jose Moon (ORCID: https://orcid.org/0009-0000-6623-5935)
- Soon Jun Hong (ORCID: https://orcid.org/0000-0003-4832-6678)
- Jong-Ho Kim (ORCID: https://orcid.org/0000-0002-1309-0821)
- Yong‐Hyun Kim (ORCID: https://orcid.org/0000-0003-1376-5128)
- Eung Ju Kim (ORCID: https://orcid.org/0000-0001-5779-6386)
Institutions
- Korea University (KR)
- Korea University Medical Center (KR)
- Ansan University (KR)
- Korea University (JP)
Publication Details
- Journal
- European Heart Journal Acute Cardiovascular Care
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1093/ehjacc/zuaf001
- Citations
- 4
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.55