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Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis
Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466966/ https://www.ncbi.nlm.nih.gov/pubmed/31065266 http://dx.doi.org/10.1155/2019/1870975 |
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author | Adadi, Amina Adadi, Safae Berrada, Mohammed |
author_facet | Adadi, Amina Adadi, Safae Berrada, Mohammed |
author_sort | Adadi, Amina |
collection | PubMed |
description | Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues. |
format | Online Article Text |
id | pubmed-6466966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-64669662019-05-07 Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis Adadi, Amina Adadi, Safae Berrada, Mohammed Adv Bioinformatics Review Article Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues. Hindawi 2019-04-02 /pmc/articles/PMC6466966/ /pubmed/31065266 http://dx.doi.org/10.1155/2019/1870975 Text en Copyright © 2019 Amina Adadi et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Adadi, Amina Adadi, Safae Berrada, Mohammed Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title | Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title_full | Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title_fullStr | Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title_full_unstemmed | Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title_short | Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis |
title_sort | gastroenterology meets machine learning: status quo and quo vadis |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466966/ https://www.ncbi.nlm.nih.gov/pubmed/31065266 http://dx.doi.org/10.1155/2019/1870975 |
work_keys_str_mv | AT adadiamina gastroenterologymeetsmachinelearningstatusquoandquovadis AT adadisafae gastroenterologymeetsmachinelearningstatusquoandquovadis AT berradamohammed gastroenterologymeetsmachinelearningstatusquoandquovadis |