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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. A...

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Main Authors: Santhanam, Nandhini (Author) , Kim, Hee Eun (Author) , Rügamer, David (Author) , Bender, Andreas (Author) , Muthers, Stefan (Author) , Cho, Chang Gyu (Author) , Alonso, Angelika (Author) , Szabo, Kristina (Author) , Centner, Franz-Simon (Author) , Wenz, Holger (Author) , Ganslandt, Thomas (Author) , Platten, Michael (Author) , Groden, Christoph (Author) , Neumaier, Michael (Author) , Siegel, Fabian (Author) , Maros, Máté E. (Author)
Format: Article (Journal)
Language:English
Published: 25 April 2025
In: npj digital medicine
Year: 2025, Volume: 8, Pages: 1-10
ISSN:2398-6352
DOI:10.1038/s41746-025-01619-w
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41746-025-01619-w
Verlag, kostenfrei, Volltext: http://www.nature.com/articles/s41746-025-01619-w
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Author Notes:Nandhini Santhanam, Hee E. Kim, David Rügamer, Andreas Bender, Stefan Muthers, Chang Gyu Cho, Angelika Alonso, Kristina Szabo, Franz-Simon Centner, Holger Wenz, Thomas Ganslandt, Michael Platten, Christoph Groden, Michael Neumaier, Fabian Siegel and Máté E. Maros
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Summary: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 (Tmin_lag3 < −2 °C; Tperceived < −1.4 °C; Tmin_lag7 > 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.
Item Description:Gesehen am 24.06.2025
Physical Description:Online Resource
ISSN:2398-6352
DOI:10.1038/s41746-025-01619-w

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