Santhanam N, Kim HE, Rügamer D, Bender A, Muthers S, Cho CG, Alonso A, Szabo K, Centner FS, Wenz H, Ganslandt T, Platten M, Groden C, Neumaier M, Siegel F, Maros ME (2025)
Publication Type: Journal article
Publication year: 2025
Book Volume: 8
Article Number: 225
Journal Issue: 1
DOI: 10.1038/s41746-025-01619-w
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
APA:
Santhanam, N., Kim, H.E., Rügamer, D., Bender, A., Muthers, S., Cho, C.G.,... Maros, M.E. (2025). Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data. npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01619-w
MLA:
Santhanam, Nandhini, et al. "Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data." npj Digital Medicine 8.1 (2025).
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