Machine learning methods in organ transplantation

Áreas de investigación:

• Applications - Health
• Methodology - Machine learning

Año:

2020

Tipo de publicación:

Artículo

Palabras clave:

Machine learning, organ transplantation, liver transplant, unos database

Autores:

• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Current Opinion in Organ Transplantation

Volumen:

25

Número:

4

Páginas:

399-405

Mes:

August

ISSN:

1087-2418

BibTex:

@article{COOTGuijo2020, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Purpose of review: Machine Learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for clinical practitioners. Recent findings: In the last 10 years, there has been an explosion of interest in the application of ML techniques to organ transplantation. Several approaches have been proposed in the literature aiming to find universal models by considering multicenter cohorts or from different countries. Moreover, recently, deep learning has also been applied demonstrating a notable ability when dealing with a vast amount of information. Summary: Organ transplantation can benefit from ML in such a way to improve the current procedures for donor-recipient matching or to improve standard scores. However, a correct preprocessing is needed to provide consistent and high quality databases for ML algorithms, aiming to robust and fair approaches to support expert decision-making systems.", awards = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", comments = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", issn = "1087-2418", journal = "Current Opinion in Organ Transplantation", keywords = "Machine learning, organ transplantation, liver transplant, unos database", month = "August", note = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", number = "4", pages = "399-405", title = "{M}achine learning methods in organ transplantation", url = "doi.org/10.1097/MOT.0000000000000774", volume = "25", year = "2020", }

Nota:

JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION

Abstract:

Purpose of review: Machine Learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for clinical practitioners. Recent findings: In the last 10 years, there has been an explosion of interest in the application of ML techniques to organ transplantation. Several approaches have been proposed in the literature aiming to find universal models by considering multicenter cohorts or from different countries. Moreover, recently, deep learning has also been applied demonstrating a notable ability when dealing with a vast amount of information. Summary: Organ transplantation can benefit from ML in such a way to improve the current procedures for donor-recipient matching or to improve standard scores. However, a correct preprocessing is needed to provide consistent and high quality databases for ML algorithms, aiming to robust and fair approaches to support expert decision-making systems.

Comentarios:

JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION