ArticleNPJ digital medicine2025
Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CLUES A Comprehensive Workflow for Integrating Geospatial Data in Biomedical Research.Nature communications · 2026Article
- Stakeholder perspectives and requirements for environmental-data-driven capacity forecasting in stroke care: a national survey of German stroke units.Frontiers in neurology · 2026Article
- Article
- PM 2.5 and black carbon associated with acute ischaemic stroke: an ecological study.BMJ neurology open · 2026Article
- From retina to brain: how deep learning closes the gap in silent stroke screening.NPJ digital medicine · 2025Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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