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 [PMID: 34316446 DOI: 10.5492/wjccm.v10.i4.112]
Corresponding Author of This Article
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
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Crit Care Med. Jul 9, 2021; 10(4): 112-119 Published online Jul 9, 2021. doi: 10.5492/wjccm.v10.i4.112
Predictive modeling in neurocritical care using causal artificial intelligence
Johnny Dang, Amos Lal, Laure Flurin, Amy James, Ognjen Gajic, Alejandro A Rabinstein
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 bythe 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
Abstract
Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
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.
