Predictive and diagnosis models of stroke from hemodynamic signal monitoring.
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...
| Published in: | Medical & Biological Engineering & Computing Vol. 59; no. 6; pp. 1325 - 1338 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150893730&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150893730 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2021 vid: 59 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150893730 150275945 150893730 NLM33987805 10.1007/s11517-021-02354-6 NLM33987805 150893730 ppf: 1325 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predictive and diagnosis models of stroke from hemodynamic signal monitoring. aug: au: García-Terriza, Luis Risco-Martín, José L. Roselló, Gemma Reig Ayala, José L. affil: Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain sug: subj: Stroke Diagnosis Tomography, X-Ray Computed Scales ab: 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 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min 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.). Graphical abstract depicting the complete process since a patient is monitored until the data collected is used to generate models. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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