Report
Selecting Interpretability Techniques for Healthcare Machine Learning models
| العنوان: | Selecting Interpretability Techniques for Healthcare Machine Learning models |
|---|---|
| المؤلفون: | Sierra-Botero, Daniel, Molina-Taborda, Ana, Valdés-Tresanco, Mario S., Hernández-Arango, Alejandro, Espinosa-Leal, Leonardo, Karpenko, Alexander, Lopez-Acevedo, Olga |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.10213 |
| رقم الانضمام: | edsarx.2406.10213 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |