Forecasting extremely high ischemic stroke incidence using
meteorological time serie

J 2024

Forecasting extremely high ischemic stroke incidence using meteorological time serie

BABALOVA, Lucia, Marian GRENDAR, Egon KURCA, Stefan SIVAK, Ema KANTOROVA et. al.

Basic information

Original name

Forecasting extremely high ischemic stroke incidence using meteorological time serie

Authors

Edition

Plos one, San Francisco, Public Library of Science, 2024, 1932-6203

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30210 Clinical neurology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.700 in 2022

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1371/journal.pone.0310018

UT WoS

001310339200002

Keywords in English

ischemic stroke; forecasting; meteorological time serie

Abstract

V originále

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.

Citovat

BABALOVA, Lucia, Marian GRENDAR, Egon KURCA, Stefan SIVAK, Ema KANTOROVA, Katarina MIKULOVA, Pavel STASTNY, Pavel FASKO, Kristina SZABOOVA, Peter KUBATKA, Slavomir NOSAL, Robert MIKULÍK and Vladimir NOSAL. Forecasting extremely high ischemic stroke incidence using meteorological time serie. Plos one. San Francisco: Public Library of Science, 2024, vol. 19, No 9, p. 1-26. ISSN 1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0310018.

@article{2439081,
   author = {Babalova, Lucia and Grendar, Marian and Kurca, Egon and Sivak, Stefan and Kantorova, Ema and Mikulova, Katarina and Stastny, Pavel and Fasko, Pavel and Szaboova, Kristina and Kubatka, Peter and Nosal, Slavomir and Mikulík, Robert and Nosal, Vladimir},
   article_location = {San Francisco},
   article_number = {9},
   doi = {http://dx.doi.org/10.1371/journal.pone.0310018},
   keywords = {ischemic stroke; forecasting; meteorological time serie},
   language = {eng},
   issn = {1932-6203},
   journal = {Plos one},
   title = {Forecasting extremely high ischemic stroke incidence using meteorological time serie},
   url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310018},
   volume = {19},
   year = {2024}
}

TY  - JOUR
ID  - 2439081
AU  - Babalova, Lucia - Grendar, Marian - Kurca, Egon - Sivak, Stefan - Kantorova, Ema - Mikulova, Katarina - Stastny, Pavel - Fasko, Pavel - Szaboova, Kristina - Kubatka, Peter - Nosal, Slavomir - Mikulík, Robert - Nosal, Vladimir
PY  - 2024
TI  - Forecasting extremely high ischemic stroke incidence using meteorological time serie
JF  - Plos one
VL  - 19
IS  - 9
SP  - 1-26
EP  - 1-26
PB  - Public Library of Science
SN  - 19326203
KW  - ischemic stroke
KW  - forecasting
KW  - meteorological time serie
UR  - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310018
N2  - 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.
ER  -

BABALOVA, Lucia, Marian GRENDAR, Egon KURCA, Stefan SIVAK, Ema KANTOROVA, Katarina MIKULOVA, Pavel STASTNY, Pavel FASKO, Kristina SZABOOVA, Peter KUBATKA, Slavomir NOSAL, Robert MIKULÍK and Vladimir NOSAL. Forecasting extremely high ischemic stroke incidence using meteorological time serie. \textit{Plos one}. San Francisco: Public Library of Science, 2024, vol.~19, No~9, p.~1-26. ISSN~1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0310018.

Detailed Information on Publication Record