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Published in: Current Neurology and Neuroscience Reports 3/2022

01-03-2022 | Stroke | Stroke (B. Ovbiagele, Section Editor)

The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature

Authors: Muideen T. Olaiya, Nita Sodhi-Berry, Lachlan L. Dalli, Kiran Bam, Amanda G. Thrift, Judith M. Katzenellenbogen, Lee Nedkoff, Joosup Kim, Monique F. Kilkenny

Published in: Current Neurology and Neuroscience Reports | Issue 3/2022

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Abstract

Purpose of Review

To critically appraise literature on recent advances and methods using “big data” to evaluate stroke outcomes and associated factors.

Recent Findings

Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication.

Summary

Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.
Appendix
Available only for authorised users
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Metadata
Title
The Allure of Big Data to Improve Stroke Outcomes: Review of Current Literature
Authors
Muideen T. Olaiya
Nita Sodhi-Berry
Lachlan L. Dalli
Kiran Bam
Amanda G. Thrift
Judith M. Katzenellenbogen
Lee Nedkoff
Joosup Kim
Monique F. Kilkenny
Publication date
01-03-2022
Publisher
Springer US
Keyword
Stroke
Published in
Current Neurology and Neuroscience Reports / Issue 3/2022
Print ISSN: 1528-4042
Electronic ISSN: 1534-6293
DOI
https://doi.org/10.1007/s11910-022-01180-z

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