Please use this identifier to cite or link to this item: http://hdl.handle.net/10668/4405
Title: Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation
Authors: Guijo-Rubio, David
Briceño, Javier
Gutiérrez, Pedro Antonio
Ayllón, Maria Dolores
Ciria, Rubén
Hervás-Martínez, César
metadata.dc.contributor.authoraffiliation: [Guijo-Rubio,D; Gutiérrez,PA; Hervás-Martínez,C] Department of Computer Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain. [Briceño,J; Ayllón,MD; Ciria,R] Unit of Hepatobiliary Surgery and Liver Transplantation, Hospital Universitario Reina Sofía, IMIBIC, Córdoba, Spain
Keywords: Support vector machine;Liver transplantation;Machine learning;Methods;Logistic models;Survival;Máquina de vectores de soporte;Trasplante de hígado;Aprendizaje automático;Métodos;Modelos logísticos;Supervivencia
metadata.dc.subject.mesh: Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::Bayes Theorem
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Decision Support Techniques::Data Interpretation, Statistical
Medical Subject Headings::Information Science::Information Science::Information Storage and Retrieval::Databases as Topic::Databases, Factual
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Clinical Laboratory Techniques::Immunologic Tests::Histocompatibility Testing
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Surgical Procedures, Operative::Digestive System Surgical Procedures::Liver Transplantation
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Models, Theoretical::Models, Statistical::Logistic Models
Medical Subject Headings::Persons::Persons::Tissue Donors
Medical Subject Headings::Health Care::Health Care Facilities, Manpower, and Services::Health Services::Tissue and Organ Procurement
Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Algorithms::Support Vector Machines
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Area Under Curve
Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Neural Networks (Computer)
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Methods
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Data Collection::Vital Statistics::Mortality::Survival Rate
Medical Subject Headings::Anthropology, Education, Sociology and Social Phenomena::Human Activities::Survival
Issue Date: 21-May-2021
Publisher: Public Library of Science
Citation: 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
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/10668/4405
metadata.dc.relation.publisherversion: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252068
metadata.dc.identifier.doi: 10.1371/journal.pone.0252068
ISSN: 1932-6203 (Online)
Appears in Collections:01- Artículos - Hospital Reina Sofía
01- Artículos - IMIBIC. Instituto Maimónides de Investigación Biomédica de Córdoba

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