Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study
EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA.
Authors
- An-Sheng Liu (ORCID: https://orcid.org/0000-0002-8927-1805)
- Chien‐Hua Huang (ORCID: https://orcid.org/0000-0003-2981-4537)
- Jen‐Tang Sun (ORCID: https://orcid.org/0000-0003-2628-9917)
- G H Chen
- Chu‐Lin Tsai (ORCID: https://orcid.org/0000-0003-4639-1513)
- Li‐Chen Fu (ORCID: https://orcid.org/0000-0002-6947-7646)
- Jinyao Gao
- Shao-Chi Lu
Institutions
- National Taiwan University (TW)
- Far Eastern Memorial Hospital (TW)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-02-28
- DOI
- https://doi.org/10.2196/67576
- Citations
- 4
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.55