Publication Name	World Journal of Critical Care Medicine
Manuscript ID	64199
Country	United States

Received	2021-02-10 22:35
Peer-Review Started	2021-02-10 22:37
To Make the First Decision
Return for Revision	2021-03-17 03:30
Revised	2021-03-17 18:08
Second Decision	2021-07-02 09:18
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-07-02 12:01
Articles in Press	2021-07-02 12:01
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-07-05 07:38
Publish the Manuscript Online	2021-07-06 23:59

ISSN	2220-3141(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/
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Category	Critical Care Medicine
Manuscript Type	Minireviews
Article Title	Predictive modeling in neurocritical care using causal artificial intelligence
Manuscript Source	Invited Manuscript
All Author List	Johnny Dang, Amos Lal, Laure Flurin, Amy James, Ognjen Gajic and Alejandro A Rabinstein
Funding Agency and Grant Number
Corresponding Author	Amos Lal, FACP, MBBS, Doctor, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, United States. lal.amos@mayo.edu
Key Words	Artificial intelligence; Digital twin; Critical care; Neurology; Causal artificial intelligence; Predictive modeling
Core Tip	The modern clinical environment is increasingly surrounded by data. The existing literature is sparse concerning the creation of a “digital twin” artificial intelligence (AI) model as a tool for education and potentially clinical decision making in the neurologic intensive care unit setting. This mini review will give readers an introduction to applications of AI inside and outside of healthcare, the idea of the “digital twin” as a model of disease, how AI has been applied in neurocritical care, and methodology for building a neurocritical care digital twin AI model that is based on a solid understanding of underlying pathophysiology.
Publish Date	2021-07-06 23:59
Citation	Dang J, Lal A, Flurin L, James A, Gajic O, Rabinstein AA. Predictive modeling in neurocritical care using causal artificial intelligence. World J Crit Care Med 2021; 10(4): 112-119
URL	https://www.wjgnet.com/2220-3141/full/v10/i4/112.htm
DOI	https://dx.doi.org/ 10.5492/wjccm.v10.i4.112

Full Article (PDF)	WJCCM-10-112.pdf
Full Article (Word)	WJCCM-10-112.docx
Manuscript File	64199_Auto_Edited-JPY.docx
Answering Reviewers	64199-Answering reviewers.pdf
Audio Core Tip	64199-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	64199-Conflict-of-interest statement.pdf
Copyright License Agreement	64199-Copyright license agreement.pdf
Peer-review Report	64199-Peer-review(s).pdf
Scientific Misconduct Check	64199-Scientific misconduct check.pdf
Scientific Editor Work List	64199-Scientific editor work list.pdf