Please use this identifier to cite or link to this item: https://repository.monashhealth.org/monashhealthjspui/handle/1/48499
Title: Editorial: Machine Learning in Action: Stroke Diagnosis and Outcome Prediction.
Authors: Abedi V.;Kawamura Y.;Li J.;Phan T.G.;Zand R.
Monash Health Department(s): Monash University - School of Clinical Sciences at Monash Health
Neurology
Institution: (Abedi) Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
(Kawamura) Department of Medicine, University of Cambridge, Cambridge, United Kingdom
(Li) Department of Molecular and Functional Genomics, Weis Center for Research, Geisinger Health System, Danville, PA, United States
(Phan) Stroke and Aging Research Group, Clinical Trials, Imaging, and Informatics Division, School of Clinical Sciences at Monash Health, Melbourne, VIC, Australia
(Phan) Department of Neurology, Monash Health, Melbourne, VIC, Australia
(Zand) Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
Issue Date: 15-Aug-2022
Copyright year: 2022
Publisher: Frontiers Media S.A.
Place of publication: Switzerland
Publication information: Frontiers in Neurology. 13 (no pagination), 2022. Article Number: 984467. Date of Publication: 20 Jul 2022.
Journal: Frontiers in Neurology
DOI: http://monash.idm.oclc.org/login?url=https://dx.doi.org/10.3389/fneur.2022.984467
URI: https://repository.monashhealth.org/monashhealthjspui/handle/1/48499
Type: Editorial
Subjects: brain hemorrhage
cardiometabolic risk
cerebrovascular accident
computer assisted tomography
convolutional neural network
coronavirus disease 2019
electronic health record
general practitioner
health care personnel
histopathology
hospital discharge
hospital readmission
medical technology
neurology
nuclear magnetic resonance imaging
recurrent disease
smoking
thorax radiography
Type of Clinical Study or Trial: Opinion, perspective or news
Appears in Collections:Articles

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