Selecting Interpretability Techniques for Healthcare Machine Learning models

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 a...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Sierra-Botero, Daniel, Molina-Taborda, Ana, Valdés-Tresanco, Mario S, Hernández-Arango, Alejandro, Espinosa-Leal, Leonardo, Karpenko, Alexander, Lopez-Acevedo, Olga
Format: Article
Language:English
Subjects:
Algorithms
Health care
Machine learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422