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The Promise of Machine Learning: When Will it be Delivered?
Oguz Akbilgic1, Robert L Davis2
1University of Tennessee Health Science Center-Oak Ridge National Laboratory Center for Biomedical Informatics, Memphis, TN 38103; Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL 60153.
Machine learning clinical decision making often matches traditional methods, not always outperforming them. Performance depends on dataset characteristics and the linearity of relationships, not just the algorithm itself.
Area of Science:
- Medical Informatics
- Biostatistics
- Machine Learning
Background:
- Clinical decision making using machine learning (ML) has not fully realized its potential.
- A key criticism is that ML does not consistently outperform traditional statistical approaches.
- This is particularly noted in predicting outcomes like mortality after heart transplant.
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