RT Journal Article
T1 Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation
A1 Guijo-Rubio, David
A1 Briceño, Javier
A1 Gutiérrez, Pedro Antonio
A1 Ayllón, Maria Dolores
A1 Ciria, Rubén
A1 Hervás-Martínez, César
K1 Support vector machine
K1 Liver transplantation
K1 Machine learning
K1 Methods
K1 Logistic models
K1 Survival
K1 Máquina de vectores de soporte
K1 Trasplante de hígado
K1 Aprendizaje automático
K1 Métodos
K1 Modelos logísticos
K1 Supervivencia
AB Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.
PB Public Library of Science
YR 2021
FD 2021-05-21
LK http://hdl.handle.net/10668/4405
UL http://hdl.handle.net/10668/4405
LA en
NO Guijo-Rubio D, Briceño J, Gutiérrez PA, Ayllón MD, Ciria R, Hervás-Martínez C. Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation. PLoS One. 2021 May 21;16(5):e0252068
DS RISalud
RD Apr 14, 2025