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

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Weather significantly impacts stroke risk, contributing to 11% of cases. Machine learning models accurately forecast daily acute ischemic stroke admissions, aiding hospital planning and patient care.

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Area of Science:

  • Environmental Health
  • Medical Informatics
  • Epidemiology

Background:

  • Weather phenomena are significant contributors to global stroke burden, accounting for approximately 11% of cases.
  • The climate crisis necessitates advanced weather-based predictive analytics for healthcare systems.
  • Accurate forecasting of disease incidence is crucial for effective public health management.

Purpose of the Study:

  • To develop and evaluate machine learning models for forecasting daily acute ischemic stroke (AIS) admissions.
  • To identify key weather parameters influencing AIS incidence.
  • To establish a generalizable framework for weather-related disease burden prediction.

Main Methods:

  • Development of predictive models using locoregional weather data and AIS patient admissions (2015-2021).
  • Geospatial matching of a 7914-patient AIS cohort from University Medical Center Mannheim, Germany, to German Weather Service data.
  • Evaluation of multiple machine learning algorithms including Poisson regression, GAMs, SVM, Random Forest, and XGBoost within a nested cross-validation framework.

Main Results:

  • Extreme Gradient Boosting (XGB) demonstrated the highest performance with a mean absolute error of 1.21 cases/day.
  • Maximum air pressure was identified as the primary predictor, while temperature showed a bimodal relationship with AIS admissions.
  • Increased AIS admissions were associated with cold stress (Tmin_lag3 < -2°C), heat stress (Tperceived < -1.4°C; Tmin_lag7 > 15°C), and stormy conditions (wind gusts > 14 m/s).

Conclusions:

  • Machine learning models effectively forecast daily acute ischemic stroke admissions based on weather data.
  • Specific weather conditions, including extreme temperatures and high winds, are linked to increased stroke incidence.
  • The developed framework offers a valuable tool for real-time hospital resource planning and predicting weather-related health impacts.