Big Data and Data Science in Critical Care

Lazaro Nelson Sanchez-Pinto, Yuan Luo, Matthew M. Churpek*

*Corresponding author for this work

Research output: Contribution to journalReview article

32 Scopus citations

Abstract

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

Original languageEnglish (US)
Pages (from-to)1239-1248
Number of pages10
JournalCHEST
Volume154
Issue number5
DOIs
StatePublished - Nov 2018

Keywords

  • big data
  • critical care
  • data science
  • machine learning
  • prediction models

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine

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