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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. Jul 9, 2021; 10(4): 112-119
Published online Jul 9, 2021. doi: 10.5492/wjccm.v10.i4.112
Published online Jul 9, 2021. doi: 10.5492/wjccm.v10.i4.112
Predictive modeling in neurocritical care using causal artificial intelligence
Johnny Dang, Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN 55905, United States
Amos Lal, Ognjen Gajic, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
Laure Flurin, Division of Clinical Microbiology, Mayo Clinic, Rochester, MN 55905, United States
Amy James, Department of Medicine, Mayo Clinic, Rochester, MN 55905, United States
Alejandro A Rabinstein, Department of Medicine, Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, United States
Author contributions: Dang J, Lal A, Flurin L and James A contributed to the manuscript draft, revision and figure selection; Lal A contributed to the critical review; Gajic O and Rabinstein AA contributed to the manuscript draft, revision, conception of idea and critical review.
Supported by the National Center for Advancing Translational Sciences , No. UL1 TR002377 .
Conflict-of-interest statement: All authors declare no conflict of interest.
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/
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
Received: February 10, 2021
Peer-review started: February 10, 2021
First decision: March 17, 2021
Revised: March 17, 2021
Accepted: July 2, 2021
Article in press: July 2, 2021
Published online: July 9, 2021
Processing time: 146 Days and 1.4 Hours
Peer-review started: February 10, 2021
First decision: March 17, 2021
Revised: March 17, 2021
Accepted: July 2, 2021
Article in press: July 2, 2021
Published online: July 9, 2021
Processing time: 146 Days and 1.4 Hours
Core Tip
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.