Predictive and diagnosis models of stroke from hemodynamic signal
  monitoring
Luis García-Terriza José L. Risco-Martín Gemma Reig Roselló José L. Ayala
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.).