Multimodal Deep Learning for Cardiovascular Risk Stratification: Integrating Retinal Biomarkers and Cardiovascular Signals for Enhanced Heart Attack Prediction
Cardiovascular diseases, particularly myocardial infarction, have remained a significant cause of death around the world. Therefore, dedicated non-invasive risk prediction frameworks are required. Conventional measures for risk prediction among most heterogeneous patients, such as the Framingham Ris...
Saved in:
| Main Authors: | K. Sathya, G. Magesh |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11025545/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
New opportunities for biomarkers in cardiovascular risk stratification. Resolution of Advisory board
by: О. M. Drapkina, et al.
Published: (2021-10-01) -
Editorial: Precision medicine: biomarker testing for diagnosis and treatment of cardiovascular disease
by: Hendrianus Hendrianus, et al.
Published: (2025-02-01) -
Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors
by: Xi-Ru Zhang, et al.
Published: (2025-04-01) -
Serum biomarkers of cardiovascular complications in COVID-19
by: R. M. Gumerov, et al.
Published: (2021-07-01) -
Multimodal Imaging in Stem Cell Therapy for Retinal Disease
by: Mi Zheng, et al.
Published: (2025-04-01)