  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.
