Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques.

ABSTRACT:

Background

Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.

Methods

In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival f

SUBMITTER: Kantidakis G 

PROVIDER: S-EPMC7667810 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature