Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Correction to: A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study
Saved in:
| Published in: | European Heart Journal : Digital Health |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Correction to: A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study
- Smarter FoCUS: AI-guided focused cardiac ultrasound enables novice detection of left ventricular dysfunction
- Transarterial embolization for chronic wrist pain: a preliminary study of feasibility, safety, and pain reduction
- Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection
- Handgrip strength in young recreational skiers: Is it related to the risk of falling?
- The hypereosinophilia dilemma: what’s beyond cardiac involvement?
- Correction to “Chinese Expert Consensus on the Use of Ultrasound‐Derived Fat Fraction in the Assessment of Metabolic Dysfunction‐Associated Steatotic Liver Disease (2025 Edition)”