Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

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, Ian Y. Lee

Research output: Contribution to journalReview articlepeer-review

17 Scopus citations

Abstract

The 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.

Original languageEnglish
Article number554633
JournalFrontiers in Neurology
Volume11
DOIs
StatePublished - 9 Oct 2020

Keywords

  • artificial intelligence
  • intensive and critical care
  • machine learning
  • neurocritical care
  • neurology

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