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Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review
81
Zitationen
16
Autoren
2022
Jahr
Abstract
ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.
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Autoren
Institutionen
- North Eastern Hill University(IN)
- National and Kapodistrian University of Athens(GR)
- Versus Arthritis(GB)
- University of Manchester(GB)
- Semmelweis University(HU)
- University of Virginia(US)
- International Institute of Information Technology(IN)
- St. Helena Hospital(US)
- Queen's University(CA)
- University of Cagliari(IT)
- Massachusetts General Hospital(US)