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.

在医疗保健领域，追求使用可解释的算法来协助医疗专业人员在多种决策场景中。针对预测性、描述性和相关性（PDR）框架，将可解释机器学习定义为一个能够明确且简单地确定数据中所含或模型所学关系对其功能和模型分类至关重要的机器学习模型，并根据后续操作和基于模型进行分类，用于训练后获取可解释性或内置于算法设计中。我们概述了八种可供此类目的使用的后续操作和基于模型的算法。