Selecting Interpretability Techniques for Healthcare Machine Learning models
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914834492162048 |
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| author | Sierra-Botero, Daniel Molina-Taborda, Ana Valdés-Tresanco, Mario S. Hernández-Arango, Alejandro Espinosa-Leal, Leonardo Karpenko, Alexander Lopez-Acevedo, Olga |
| author_facet | Sierra-Botero, Daniel Molina-Taborda, Ana Valdés-Tresanco, Mario S. Hernández-Arango, Alejandro Espinosa-Leal, Leonardo Karpenko, Alexander Lopez-Acevedo, Olga |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Selecting Interpretability Techniques for Healthcare Machine Learning models Sierra-Botero, Daniel Molina-Taborda, Ana Valdés-Tresanco, Mario S. Hernández-Arango, Alejandro Espinosa-Leal, Leonardo Karpenko, Alexander Lopez-Acevedo, Olga Machine Learning 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. |
| title | Selecting Interpretability Techniques for Healthcare Machine Learning models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.10213 |