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

Background In the midst of the emerging climate crisis, healthcare providers lack locally validated, disease-specific surveillance models. Stroke, a significant contributor to the global disease burden, has been linked to climate change. Therefore, we developed and benchmarked machine learning (ML) models based on locoregional weather systems to forecast the number of daily acute ischemic stroke (AIS) admissions. Methods AIS patients diagnosed between 2015 and 2021 at the tertiary University Medical Center (UMC) Mannheim, Germany were extracted from the local data integration center and geospatially matched to weather data from the German Weather Service (DWD) based on the clinic’s, patients’ home and closest tower’s locations at the time of admission. Statistical- (Poisson), boosted generalized additive model (GAM), support vector machines (SVR), and tree-based models including random forest (RF) and extreme gradient boosting (XGB) were evaluated in regression settings within time-stratified nested cross-validation setup (training-validation: 2015-2020, test set: 2021) to predict the number of daily AIS admissions. Findings The cohort included 7,914 AIS patients (4,244 male, 53·6%). XGB showed the best test performance with lowest mean absolute error (MAE) of 1·21 cases/day. Maximum air pressure was identified as the top predictive variable. Shapley additive explanations analyses revealed that temperature extremes of extended cold-(lag-3 minimum temperature <-2 °C; minimum perceived temperature 15 °C), as well as stormy conditions (lag-1 and lag-2 maximum wind gust >14 m/s and speed >10·4 m/s), increased stroke incidences substantially with distinct seasonal associations. Interpretation ML models can sufficiently forecast AIS admissions based on weather patterns allowing for improved resource allocation and preparedness.