Srichawla BS. Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning. World J Crit Care Med 2024; 13(2): 91397 [PMID: 38855276 DOI: 10.5492/wjccm.v13.i2.91397]
Corresponding Author of This Article
Bahadar S Srichawla, DO, MS, Staff Physician, Department of Neurology, University of Massachusetts Chan Medical School, 55 Lake Ave N, Worcester, MA 01655, United States. bahadar.srichawla@umassmemorial.org
Research Domain of This Article
Clinical Neurology
Article-Type of This Article
Review
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. Jun 9, 2024; 13(2): 91397 Published online Jun 9, 2024. doi: 10.5492/wjccm.v13.i2.91397
Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning
Bahadar S Srichawla
Bahadar S Srichawla, Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01655, United States
Author contributions: Srichawla BS designed and completed the literature review, completed data synthesis, generated figures and tables for the manuscript, and wrote the manuscript.
Conflict-of-interest statement: Bahadar Srichawla reports having no conflicts 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bahadar S Srichawla, DO, MS, Staff Physician, Department of Neurology, University of Massachusetts Chan Medical School, 55 Lake Ave N, Worcester, MA 01655, United States. bahadar.srichawla@umassmemorial.org
Received: December 27, 2023 Revised: January 27, 2024 Accepted: March 6, 2024 Published online: June 9, 2024 Processing time: 158 Days and 13.4 Hours
Core Tip
Core Tip: This manuscript provided a comprehensive review of multimodal monitoring (MMM) in the intensive care unit, emphasizing the integration of neurophysics to optimize patient outcomes. It covered invasive and noninvasive neuromonitoring tools and highlighted the role of machine learning in real-time data analysis and interpretation from MMM tools, aiding in precise clinical decision-making. By integrating diverse data streams through MMM, machine learning algorithms enhance the understanding of cerebral physiology and disease, offering invaluable insights for personalized patient care in the intensive care unit. This integration aids the neurointensivist in more accurate neuroprognostication and in future avenues for targeted therapeutic interventions.
