Predictive and diagnosis models of stroke from hemodynamic signal monitoring

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 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.).

Tasks

Reproductions