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6/28/2022 9:38:07 AM | Browse: 333 | Download: 772
Publication Name Artificial Intelligence in Gastroenterology
Manuscript ID 74644
Country/Territory Brazil
Received
2021-12-31 02:21
Peer-Review Started
2021-12-31 02:22
To Make the First Decision
Return for Revision
2022-03-28 05:40
Revised
2022-04-15 16:35
Second Decision
2022-05-07 07:56
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2022-05-08 07:14
Articles in Press
2022-05-08 07:14
Publication Fee Transferred
Edit the Manuscript by Language Editor
2022-05-01 00:39
Typeset the Manuscript
2022-05-26 06:45
Publish the Manuscript Online
2022-06-28 09:38
ISSN 2644-3236 (online)
Open Access This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Gastroenterology & Hepatology
Manuscript Type Minireviews
Article Title Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases
Manuscript Source Invited Manuscript
All Author List Randhall B Carteri, Mateus Grellert, Daniela Luisa Borba, Claudio Augusto Marroni and Sabrina Alves Fernandes
ORCID
Author(s) ORCID Number
Randhall B Carteri http://orcid.org/0000-0003-4124-9470
Mateus Grellert http://orcid.org/0000-0003-0600-7054
Daniela Luisa Borba http://orcid.org/(0000-0002- 2141-3993
Claudio Augusto Marroni http://orcid.org/0000-0002-1718-6548
Sabrina Alves Fernandes http://orcid.org/0000-0001-8504-603X
Funding Agency and Grant Number
Corresponding Author Sabrina Alves Fernandes, PhD, Research Scientist, Teacher, Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Sarmento Leite street, 245 - Centro Histórico, Porto Alegre 90050-170, Rio Grande do Sul, Brazil. sabrinaafernandes@gmail.com
Key Words Artificial intelligence; Liver diseases; Healthcare; Hepatology; Prognosis; Diagnostics
Core Tip The ability of machine learning approaches to process multiple variables, map linear and nonlinear interactions, ranking the most important features, in addition to the capability of building accurate prediction models, sets a future direction to its application in complex diseases such as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science.
Publish Date 2022-06-28 09:38
Citation Carteri RB, Grellert M, Borba DL, Marroni CA, Fernandes SA. Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases. Artif Intell Gastroenterol 2022; 3(3): 80-87
URL https://www.wjgnet.com/2644-3236/full/v3/i3/80.htm
DOI https://dx.doi.org/10.35712/aig.v3.i3.80
Full Article (PDF) AIG-3-80.pdf
Full Article (Word) AIG-3-80.docx
Manuscript File 74644_Auto_Edited-LJH-FilipodiaCL.docx
Answering Reviewers 74644-Answering reviewers.pdf
Audio Core Tip 74644-Audio core tip.m4a
Conflict-of-Interest Disclosure Form 74644-Conflict-of-interest statement.pdf
Copyright License Agreement 74644-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate 74644-Language certificate.pdf
Peer-review Report 74644-Peer-review(s).pdf
Scientific Misconduct Check 74644-Bing-Liu JH-2.jpg
Scientific Misconduct Check 74644-CrossCheck.jpg
Scientific Editor Work List 74644-Scientific editor work list.pdf