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

2022-03

Olaiya, Muideen T

Sodhi-Berry, Nita

Dalli, Lachlan L

Bam, Kiran

Thrift, Amanda G

Katzenellenbogen, Judith M

Nedkoff, Lee

Kim, Joosup

Kilkenny, Monique F

Big data

Machine learning

Mortality

Outcomes

Stroke

Validation studies

Journal Article

http://orcid.org/0000-0002-4070-0533

http://orcid.org/0000-0003-3406-6019

http://orcid.org/0000-0003-1449-9132

http://orcid.org/0000-0002-3970-625X

http://orcid.org/0000-0001-8533-4170

0000-0002-4079-0428

0000-0002-3375-287X

10.1007/s11910-022-01180-z

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

link

Current Neurology and Neuroscience Reports 2022; 22(3): 151-160

Current neurology and neuroscience reports