Selecting Interpretability Techniques for Healthcare Machine Learning models
This work addresses the need for interpretable algorithms to assist healthcare professionals in decision-making, but it is incremental as it primarily reviews and categorizes existing techniques.
The paper tackles the problem of selecting interpretability techniques for healthcare machine learning models by applying the Predictive, Descriptive, and Relevant (PDR) framework to categorize and overview eight algorithms, including post-hoc and model-based methods, without providing specific numerical results.
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.