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Big Data Defined: A Practical Review for Neurosurgeons

  • Mohamad Bydon
  • , Clemens M. Schirmer
  • , Eric K. Oermann
  • , Ryan S. Kitagawa
  • , Nader Pouratian
  • , Jason Davies
  • , Ashwini Sharan
  • , Lola B. Chambless
  • Mayo Clinic Rochester, MN
  • Geisinger Medical Center
  • Icahn School of Medicine at Mount Sinai
  • University of Texas Health Science Center at Houston
  • University of California at Los Angeles
  • Thomas Jefferson University
  • Vanderbilt University

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Background: Modern science and healthcare generate vast amounts of data, and, coupled with the increasingly inexpensive and accessible computing, a tremendous opportunity exists to use these data to improve care. A better understanding of data science and its relationship to neurosurgical practice will be increasingly important as we transition into this modern “big data” era. Methods: A review of the literature was performed for key articles referencing big data for neurosurgical care or related topics. Results: In the present report, we first defined the nature and scope of data science from a technical perspective. We then discussed its relationship to the modern neurosurgical practice, highlighting key references, which might form a useful introductory reading list. Conclusions: Numerous challenges exist going forward; however, organized neurosurgery has an important role in fostering and facilitating these efforts to merge data science with neurosurgical practice.

Original languageEnglish
Pages (from-to)e842-e849
JournalWorld Neurosurgery
Volume133
DOIs
StatePublished - Jan 2020

Keywords

  • Clinical practice
  • Data science
  • Machine learning
  • Registry

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