# Predictive and diagnosis models of stroke from hemodynamic signal monitoring

arXiv: https://arxiv.org/abs/2306.05289 · HTML: https://mlsanity.com/paper/2306.05289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 26 references that are in the map.
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 1 of 1)

- [Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression](https://mlsanity.com/paper/2403.09758/) (2024)

## Abstract (arXiv)

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 acrshortCT 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.).