Forecasting extremely high ischemic stroke incidence using meteorological time serie

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F24%3A00137245" target="_blank" >RIV/00216224:14110/24:00137245 - isvavai.cz</a>

Result on the web

<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310018" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0310018" target="_blank" >10.1371/journal.pone.0310018</a>

Result language

angličtina

Original language name

Forecasting extremely high ischemic stroke incidence using meteorological time serie

Original language description

Motivation The association between weather conditions and stroke incidence has been a subject of interest for several years, yet the findings from various studies remain inconsistent. Additionally, predictive modelling in this context has been infrequent. This study explores the relationship of extremely high ischaemic stroke incidence and meteorological factors within the Slovak population. Furthermore, it aims to construct forecasting models of extremely high number of strokes.Methods Over a five-year period, a total of 52,036 cases of ischemic stroke were documented. Days exhibiting a notable surge in ischemic stroke occurrences (surpassing the 90th percentile of historical records) were identified as extreme cases. These cases were then scrutinized alongside daily meteorological parameters spanning from 2015 to 2019. To create forecasts for the occurrence of these extreme cases one day in advance, three distinct methods were employed: Logistic regression, Random Forest for Time Series, and Croston's method.Results For each of the analyzed stroke centers, the cross-correlations between instances of extremely high stroke numbers and meteorological factors yielded negligible results. Predictive performance achieved by forecasts generated through multivariate logistic regression and Random Forest for time series analysis, which incorporated meteorological data, was on par with that of Croston's method. Notably, Croston's method relies solely on the stroke time series data. All three forecasting methods exhibited limited predictive accuracy.Conclusions The task of predicting days characterized by an exceptionally high number of strokes proved to be challenging across all three explored methods. The inclusion of meteorological parameters did not yield substantive improvements in forecasting accuracy.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30210 - Clinical neurology

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plos one

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

19

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

26

Pages from-to

1-26

UT code for WoS article

001310339200002

EID of the result in the Scopus database

2-s2.0-85203658903