Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

PurposeAccurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available f...

Full description

Bibliographic Details
Main Authors: Murad Megjhani, Kalijah Terilli, Hans-Peter Frey, Angela G. Velazquez, Kevin William Doyle, Edward Sander Connolly, David Jinou Roh, Sachin Agarwal, Jan Claassen, Noemie Elhadad, Soojin Park
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-03-01
Series:Frontiers in Neurology
Subjects:
subarachnoid hemorrhage
convolutional dictionary learning
time series
machine learning
critical care
Online Access:http://journal.frontiersin.org/article/10.3389/fneur.2018.00122/full

Internet

http://journal.frontiersin.org/article/10.3389/fneur.2018.00122/full

Similar Items