Predictive Analytics
Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data
https://www.medrxiv.org/content/10.1101/2024.07.03.24309252v1
Power Pitch & Poster presentations, ID 259 & 325, 2023-10-05 & 06
Predicting ischemic stroke admissions based on weather patterns for improved clinical resource allocation using machine learning
Background: Weather factors like temperature, pressure, and humidity contribute to stroke risk, which will be potentiated by the impending global climate crisis [1]. Our study aimed to predict the number of ischemic stroke cases at clinically relevant time resolutions in order to improve healthcare resource allocation. [ ...]
- Local results from the WEather-based STroke event and Outcome Risk Modeling (WE-STORM) project (PI Dr. Maros).
Poster presentation, P0165 Epidemiology & Risk Factors, 2024-05-16
Rainstorms with a splash of blood: machine learning-based predictive analytics for hemorrhagic stroke admission based on weather systems
Background: The climate crisis impacts cardiovascular health and stroke, contributing significantly to the global disease burden. Weather-based disease surveillance for healthcare providers is lacking.
Aim: Hence, we aimed to forecast the number of daily hemorrhagic stroke admissions based on meteorological parameters by applying machine learning models. [...]
- Local results from the WEather-based STroke event and Outcome Risk Modeling (WE-STORM) project (PI Dr. Maros).
Poster as PDF is available here:
