AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients
AI-ECG age is a strong and independent predictor of long-term mortality in cardiovascular and acute care patients.
Authors
- Clemens Dlaska (ORCID: https://orcid.org/0000-0002-8798-0827)
- Michael Schreinlechner (ORCID: https://orcid.org/0000-0001-5251-6971)
- Axel Bauer (ORCID: https://orcid.org/0000-0001-9201-8555)
- Florian Hofer (ORCID: https://orcid.org/0000-0003-2532-0919)
- Mercedes Gauthier (ORCID: https://orcid.org/0000-0003-1431-0045)
- Sebastian J. Reinstadler (ORCID: https://orcid.org/0000-0002-7700-1357)
- Patrick Rockenschaub (ORCID: https://orcid.org/0000-0002-6499-7933)
- Angus Nicolson (ORCID: https://orcid.org/0009-0008-5128-9680)
- Fabian Theurl (ORCID: https://orcid.org/0000-0002-0190-5958)
- Samuel Pröll (ORCID: https://orcid.org/0000-0003-3074-8637)
- Daniel Pavluk
Institutions
- Innsbruck Medical University (AT)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-09-27
- DOI
- https://doi.org/10.1093/ehjdh/ztaf109
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31