Machine learning support for kidney transplantation decision making

Abstract

Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straight-forward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver's acceptance of the transplanted organ. Currently the process of analysis of these variables involves a careful study by the clinical transplant team. The number and complexity of causal dependencies among variables make the manual process very slow. In this paper we assess the usefulness of Machine Learning algorithms as a tool to improve and speed up the decisions of a transplant team. We achieve that objective by analysing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts.