• Farhan Chaudhry, Rachel J Hunt, Prashant Hariharan, Sharath Kumar Anand, Surya Sanjay, Ellen E Kjoller, Connor M Bartlett, Kipp W Johnson, Phillip D Levy, Houtan Noushmehr, and Ian Y Lee.
• Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, United States.
• Front Neurol. 2020 Jan 1; 11: 554633.

AbstractThe neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.Copyright © 2020 Chaudhry, Hunt, Hariharan, Anand, Sanjay, Kjoller, Bartlett, Johnson, Levy, Noushmehr and Lee.

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