Selecting Interpretability Techniques for Healthcare Machine Learning models

• URL: http://arxiv.org/abs/2406.10213v1
• Date: Fri, 14 Jun 2024 17:49:04 GMT
• Title: Selecting Interpretability Techniques for Healthcare Machine Learning models
• Authors: Daniel Sierra-Botero, Ana Molina-Taborda, Mario S. Valdés-Tresanco, Alejandro Hernández-Arango, Leonardo Espinosa-Leal, Alexander Karpenko, Olga Lopez-Acevedo,
• Abstract summary: In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes.
• Score: 69.65384453064829
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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