TY - JOUR
T1 - Big Data and Data Science in Critical Care
AU - Sanchez-Pinto, Lazaro Nelson
AU - Luo, Yuan
AU - Churpek, Matthew M.
N1 - Funding Information:
Financial/nonfinancial disclosures: The authors have reported to CHEST the following: M. M. C. has a patent pending for risk stratification algorithms for hospitalized patients; he is also supported by a career development award from the National Heart, Lung and Blood Institute and a research project grant program award from the National Institute of General Medical Sciences . None declared (L. N. S.-P., Y. L.).
Publisher Copyright:
© 2018 American College of Chest Physicians
PY - 2018/11
Y1 - 2018/11
N2 - 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.
AB - 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.
KW - big data
KW - critical care
KW - data science
KW - machine learning
KW - prediction models
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U2 - 10.1016/j.chest.2018.04.037
DO - 10.1016/j.chest.2018.04.037
M3 - Review article
C2 - 29752973
AN - SCOPUS:85051128249
SN - 0012-3692
VL - 154
SP - 1239
EP - 1248
JO - Diseases of the chest
JF - Diseases of the chest
IS - 5
ER -