Early stroke detection through machine learning in the prehospital setting.

Background: Stroke is a leading cause of death and disability globally, with rising prevalence driven by modern lifestyle factors. Despite the critical nature of stroke as a time-sensitive condition requiring prompt diagnosis and intervention, current pre-diagnostic practices are often limited by reliance on specific patient symptoms, which can delay appropriate treatment, especially for large vessel occlusions (LVO). This study introduces a novel approach utilizing machine learning techniques to accurately identify stroke type and severity using hemodynamic data. By enhancing the pre-hospital diagnosis process, the research aims to optimize hospital selection and improve emergency stroke care, ultimately ensuring timely treatment at specialized centers. Methods: The methodology of this project consists on two phases. The first step involves developing two specialized models to predict the type of stroke-ischemic or hemorrhagic-along with a Bayesian rule to determine the final classification. The second step, applied only in cases of ischemic stroke, identifies whether the episode is a Large Vessel Occlusion (LVO) or not. Results: The study developed a robust framework for detecting Large Vessel Occlusions (LVO) during Emergency Medical Services (EMS) interventions. The results for ischemic episodes showed that the LVO model achieved 91.67% recall and 64.71% precision, outperforming the prehospital scale used as a reference in all performance metrics except specificity. This model utilized only 20 out of the 271 original variables, with the most representative variables including blood pressure, heart rate, oxygen saturation, and arm movement. The integration of the LVO model for the complete sample with a Bayesian pipeline resulted in a precision of 59% and a recall of 74%, while applying the LVO model to the entire population yielded a precision of 60.60% and a recall of 80.19%. Conclusion: The study concluded that the implementation of Machine Learning (ML) techniques can significantly improve the diagnostic accuracy of stroke in the context of Emergency Medical Services (EMS). The LVO model demonstrated promising results, with an improvement in positive recall of approximately 10%-13% compared to the baseline paradigm. The use of objective variables, such as blood pressure and heart rate, was a key factor in this enhancement. The study highlights the potential benefits of leveraging ML techniques in Emergency Medicine, particularly in the diagnosis and management of stroke. The results suggest that the LVO model can potentially augment the precision of stroke diagnosis, facilitating more efficacious and timely interventions.