Prediction and early identification of stroke is crucial to prevent emergency department (ED) revisits and initiate treatment, reducing morbidity and mortality.
This project focuses on the analysis of non-contrast brain CT (NCCT) data to predict early ED revisits for patients coming back with a stroke diagnosis. The first objective will be gathering open-source NCCT data as well as NCCT data from the Integrated Health and Social Services Center from Chaudiere-Appalaches (CISSS-CA) to classify the presence/absence of stroke using an existing model. The second objective will be to develop and test a machine learning model with weights from the previous model and other relevant clinical data to classify short-term revisits to the ED as an outcome.
From a clinical perspective, the development of such a tool may help support neuroradiologists in image interpretation and clinical decision making in the ED.