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Título : Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.
Autor : Guijo-Rubio, David
Briceño, Javier
Gutiérrez, Pedro Antonio
Ayllón, Maria Dolores
Ciria, Rubén
Hervás-Martínez, César
MeSH: Bayes Theorem
Data Interpretation, Statistical
Databases, Factual
Histocompatibility Testing
Liver Transplantation
Logistic Models
Support Vector Machine
Tissue Donors
Tissue and Organ Procurement
Transplant Recipients
Fecha de publicación : 21-may-2021
Abstract: 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.
URI: http://hdl.handle.net/10668/17815
DOI: 10.1371/journal.pone.0252068
Aparece en las colecciones: Producción 2020

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