Publication Name	World Journal of Gastroenterology
Manuscript ID	65354
Country	Australia

Received	2021-03-05 03:40
Peer-Review Started	2021-03-05 03:46
To Make the First Decision
Return for Revision	2021-04-17 13:42
Revised	2021-04-26 03:06
Second Decision	2021-09-06 03:01
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2021-09-06 11:05
Articles in Press	2021-09-06 11:05
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-10-09 03:15
Publish the Manuscript Online	2021-10-12 06:53

ISSN	1007-9327 (print) and 2219-2840 (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) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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Website	http://www.wjgnet.com

Category	Gastroenterology & Hepatology
Manuscript Type	Observational Study
Article Title	Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study
Manuscript Source	Invited Manuscript
All Author List	Danny Con, Daniel R van Langenberg and Abhinav Vasudevan
ORCID	Author(s) ORCID Number Danny Con http://orcid.org/0000-0002-4983-6103 Daniel R van Langenberg http://orcid.org/0000-0003-3662-6307 Abhinav Vasudevan http://orcid.org/0000-0001-5026-9014
Funding Agency and Grant Number
Corresponding Author	Danny Con, MD, Doctor, Doctor, Statistician, Department of Gastroenterology and Hepatology, Eastern Health, 8 Arnold Street, Box Hill 3128, Victoria, Australia. dannycon302@gmail.com
Key Words	Machine learning; Artificial intelligence; Precision medicine; Personalized medicine; Deep learning
Core Tip	Deep learning has vast potential, but its clinical utility in predicting outcomes in Crohn’s disease (CD) has not been explored. This study showed that deep learning algorithms (a recurrent neural network) using a more complex information structure including repeated biomarker measurements had a better predictive performance compared to a conventional statistical algorithm using only baseline data. This proof-of-concept study therefore paves the way for further research in the use of deep learning methods in clinical prediction in CD.
Publish Date	2021-10-12 06:53
Citation	Con D, van Langenberg DR, Vasudevan A. Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study. World J Gastroenterol 2021; 27(38): 6476-6488
URL	https://www.wjgnet.com/1007-9327/full/v27/i38/6476.htm
DOI	https://dx.doi.org/10.3748/wjg.v27.i38.6476

Full Article (PDF)	WJG-27-6476.pdf
Full Article (Word)	WJG-27-6476.docx
STROBE Statement	65354-STROBE-Statement-revision.pdf
Manuscript File	65354_Auto_Edited-MLS.docx
Answering Reviewers	65354-Answering reviewers.pdf
Audio Core Tip	65354-Audio core tip.mp3
Biostatistics Review Certificate	65354-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	65354-Conflict-of-interest statement.pdf
Copyright License Agreement	65354-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s)	65354-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	65354-Institutional review board statement.pdf
Peer-review Report	65354-Peer-review(s).pdf
Scientific Editor Work List	65354-Scientific editor work list.pdf
CrossCheck Report	65354-CrossCheck report.pdf