Other Titles
Rising Stars of Research and Scholarship Invited Student Posters
Abstract
This systematic review sought to determine if machine learning models more accurately predict in-hospital cardiac arrest when compared to the modified early warning score. Five of five studies demonstrated that machine learning models more accurately predicted cardiac arrest hours before the event occurred with fewer false alarms.
Sigma Membership
Non-member
Lead Author Affiliation
Purdue University, West Lafayette, Indiana, USA
Type
Poster
Format Type
Text-based Document
Study Design/Type
Systematic Review
Research Approach
N/A
Keywords:
Cardiac Arrest, Machine Learning, Prediction
Recommended Citation
Moffat, Laura Marie, "The accuracy of machine learning to predict cardiac arrest: A systematic review" (2019). Convention. 273.
https://www.sigmarepository.org/convention/2019/posters_2019/273
Conference Name
45th Biennial Convention
Conference Host
Sigma Theta Tau International
Conference Location
Washington, DC, USA
Conference Year
2019
Rights Holder
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All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Acquisition
Proxy-submission
The accuracy of machine learning to predict cardiac arrest: A systematic review
Washington, DC, USA
This systematic review sought to determine if machine learning models more accurately predict in-hospital cardiac arrest when compared to the modified early warning score. Five of five studies demonstrated that machine learning models more accurately predicted cardiac arrest hours before the event occurred with fewer false alarms.
Description
45th Biennial Convention 2019 Theme: Connect. Collaborate. Catalyze.