Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study
Ultrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.
Authors
- Ankush D. Jamthikar (ORCID: https://orcid.org/0000-0002-3030-7236)
- Jeffrey L. Carson (ORCID: https://orcid.org/0000-0001-5466-2833)
- Naveena Yanamala (ORCID: https://orcid.org/0000-0002-4454-4240)
- Quincy A. Hathaway (ORCID: https://orcid.org/0000-0001-8226-2319)
- Bernard Chaitman (ORCID: https://orcid.org/0000-0002-9216-5317)
- Partho P. Sengupta (ORCID: https://orcid.org/0000-0003-2291-5001)
- Nivedita Rajiv
Institutions
- Rutgers, The State University of New Jersey (US)
- University of Missouri–St. Louis (US)
- Johnson University (US)
- Saint Louis University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Echo Research and Practice
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s44156-024-00057-w
- Citations
- 6
- Primary Topic
- Ultrasound in Clinical Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.53
Funders
- National Science Foundation