Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department
Our study demonstrates the effectiveness of deep learning in predicting cardiac arrest in emergency department. Despite the higher clinical value associated with detecting patients requiring CPR, there is a scarcity of literature utilizing deep learning in CPR detection tasks. Therefore, this study embarks on an initial exploration into the task of CPR detection.
Authors
- Jung-Der Wang
- Chien‐Hua Huang (ORCID: https://orcid.org/0000-0003-2981-4537)
- Chia‐Hsin Ko
- Yuan-Xiang Deng (ORCID: https://orcid.org/0009-0006-1151-2097)
- Chu‐Lin Tsai (ORCID: https://orcid.org/0000-0003-4639-1513)
- Li‐Chen Fu (ORCID: https://orcid.org/0000-0002-6947-7646)
Institutions
- National Taiwan University (TW)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- BioData Mining
- Published
- 2024-11-23
- DOI
- https://doi.org/10.1186/s13040-024-00407-8
- Citations
- 7
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.84
Funders
- Ministry of Science and Technology, Taiwan
- National Health Research Institutes
- National Taiwan University Hospital
- National Taiwan University
- National Science and Technology Council