Explainable Machine Larning for liver transplantation
  In this work, we present a flexible method for explaining, in human readable
terms, the predictions made by decision trees used as decision support in liver
transplantation. The decision trees have been obtained through machine learning
applied on a dataset collected at the liver transplantation unit at the
Coru\~na University Hospital Center and are used to predict long term (five
years) survival after transplantation. The method we propose is based on the
representation of the decision tree as a set of rules in a logic program (LP)
that is further annotated with text messages. This logic program is then
processed using the tool xclingo (based on Answer Set Programming) that allows
building compound explanations depending on the annotation text and the rules
effectively fired when a given input is provided. We explore two alternative LP
encodings: one in which rules respect the tree structure (more convenient to
reflect the learning process) and one where each rule corresponds to a
(previously simplified) tree path (more readable for decision making).
