Predictive and diagnosis models of stroke from hemodynamic signal monitoring (2306.05289v1)

Abstract: This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus during the first 3 hours of monitoring, and to predict the stroke recurrence in just 15 minutes of monitoring. Patients with difficult access to a \acrshort{CT} scan, and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around $98\%$ precision ($97.8\%$ Sensitivity, $99.5\%$ Specificity), exitus prediction with $99.8\%$ precision ($99.8\%$ Sens., $99.9\%$ Spec.) and $98\%$ precision predicting stroke recurrence ($98\%$ Sens., $99\%$ Spec.).