Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data

The climate crisis underscores the need for weather-based predictive analytics in healthcare, as weather factors contribute to ~11% of the global stroke burden. Therefore, we developed machine learning models using locoregional weather data to forecast daily acute ischemic stroke (AIS) admissions. An AIS cohort of 7914 patients admitted between 2015 and 2021 at the tertiary University Medical Center Mannheim, Germany, with a 600,000-population catchment area, was geospatially matched to German Weather Service data. Poisson regression, boosted generalized additive models, support vector machines, random forest, and extreme gradient boosting (XGB) were evaluated within a time-stratified nested cross-validation framework. XGB performed best (mean absolute error: 1.21 cases/day). Maximum air pressure was the top predictor, with temperature exhibiting a bimodal link. Cold and heat stressor days (T<sub>min_lag3</sub> < -2 °C; T<sub>perceived</sub> < -1.4 °C; T<sub>min_lag7</sub> > 15 °C) and stormy conditions (wind gusts > 14 m/s) increased stroke admissions. This generalizable framework could aid real-time hospital planning, effective care and forecasting of various weather-related disease burdens.

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Publication Details

Journal
npj Digital Medicine
Published
2025-04-25
DOI
https://doi.org/10.1038/s41746-025-01619-w
Citations
2
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
2.40

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article

Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data

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2 citations
npj Digital Medicine
Climate Change and Health Impacts
2.40
article

Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data

Christoph Groden, Mate Elod Maros, Stefan Muthers, Holger Wenz, Franz-Simon Centner, Andreas Bender, Michael Platten, Kristina Szabo, David Rügamer, Fabian Siegel, Thomas Ganslandt, Nandhini Santhanam, Michael Neumaier, Kim Eun Hee, Angelika Alonso, Chang Gyu Cho
article en
2 citations

Abstract

The climate crisis underscores the need for weather-based predictive analytics in healthcare, as weather factors contribute to ~11% of the global stroke burden. Therefore, we developed machine learning models using locoregional weather data to forecast daily acute ischemic stroke (AIS) admissions. An AIS cohort of 7914 patients admitted between 2015 and 2021 at the tertiary University Medical Center Mannheim, Germany, with a 600,000-population catchment area, was geospatially matched to German Weather Service data. Poisson regression, boosted generalized additive models, support vector machines, random forest, and extreme gradient boosting (XGB) were evaluated within a time-stratified nested cross-validation framework. XGB performed best (mean absolute error: 1.21 cases/day). Maximum air pressure was the top predictor, with temperature exhibiting a bimodal link. Cold and heat stressor days (T<sub>min_lag3</sub> < -2 °C; T<sub>perceived</sub> < -1.4 °C; T<sub>min_lag7</sub> > 15 °C) and stormy conditions (wind gusts > 14 m/s) increased stroke admissions. This generalizable framework could aid real-time hospital planning, effective care and forecasting of various weather-related disease burdens.

npj Digital MedicineVol. 8(1)
Deutscher Wetterdienst (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), LMU Klinikum (DE), Zimmer Biomet (Germany) (DE), University Medical Centre Mannheim (DE), Munich Center for Machine Learning, Ludwig-Maximilians-Universität München (DE)
Bundesministerium für Bildung und Forschung
Climate action
Openalex Percentile: Top 13%
Climate Change and Health Impacts
2.40
DOIOpen Access PDFOpenAlex
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