1. Selecting Interpretability Techniques for Healthcare Machine Learning models
- Author
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Sierra-Botero, Daniel, Molina-Taborda, Ana, Valdés-Tresanco, Mario S., Hernández-Arango, Alejandro, Espinosa-Leal, Leonardo, Karpenko, Alexander, and Lopez-Acevedo, Olga
- Subjects
Computer Science - Machine Learning - Abstract
In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. Following the Predictive, Descriptive and Relevant (PDR) framework, the definition of interpretable machine learning as a machine-learning model that explicitly and in a simple frame determines relationships either contained in data or learned by the model that are relevant for its functioning and the categorization of models by post-hoc, acquiring interpretability after training, or model-based, being intrinsically embedded in the algorithm design. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes., Comment: 26 pages, 5 figures
- Published
- 2024