Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	74644
Country	Brazil

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
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.
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

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

ISSN	2644-3236 (online)
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