Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching

Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) matching.

Artificial intelligence models can show a great advantage by being able to handle a multitude of variables, be objective and help in cases of similar probabilities. In the field of liver transplantation, the most commonly used classifiers have been artificial neural networks (ANNs) and random forest classifiers. ANNs are excellent tools for finding patterns which are far too complex for a clinician and are capable of generating near-perfect predictions on the data on which they are fit, yielding excellent prediction capabilities reaching 95% for 3 months graft survival. On the other hand, RF can overcome ANNs in some of their limitations, mainly because of the lack of information on the variables they provide. Random forest algorithms may allow for improved confidence with the use of marginal organs and better outcome after transplantation.

ANNs and random forest can handle a multitude of structured and unstructured parameters, and establish non explicit relationships among risk factors of clinical relevance.