Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest

Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest

Background: During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25–42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous circulati...

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Bibliographic Details
Main Authors: Jon Urteaga, Andoni Elola, Anders Norvik, Eirik Unneland, Trygve C. Eftestøl, Abhishek Bhardwaj, David Buckler, Benjamin S. Abella, Eirik Skogvoll, Elisabete Aramendi
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:Resuscitation Plus
Subjects:
Pulseless electrical activity (PEA)
Machine Learning models
Cardiopulmonary resuscitation (CPR)
Evolution prediction
Online Access:http://www.sciencedirect.com/science/article/pii/S2666520424000493

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http://www.sciencedirect.com/science/article/pii/S2666520424000493

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