The Accuracy of Machine Learning to Predict Cardiac Arrest: A Systematic Review
Other Title(s)
Rising Stars of Research and Scholarship Invited Student Posters
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Author Information
- Author(s)
- Details
-
Laura Marie Moffat, MSN - School of Nursing, Purdue University School of Nursing, West Lafayette, IN, USA
- Sigma Affiliation
- Non-member
- Contributor Affiliation(s)
- Purdue University, West Lafayette, Indiana, USA
Item Information
Item Link - Use this link for citations and online mentions.
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.
Description
45th Biennial Convention 2019 Theme: Connect. Collaborate. Catalyze.
Repository Posting Date
2019-11-07T17:56:23Z
Type Information
| Type | Poster |
| Acquisition | Proxy-submission |
| Review Type | None: Event Material, Invited Presentation |
| Format | Text-based Document |
Category Information
| Evidence Level | Systematic Review |
| Research Approach | N/A |
| Keywords | Cardiac Arrest; Machine Learning; Prediction |
Conference Information
| Name | 45th Biennial Convention |
| Host | Sigma Theta Tau International |
| Location | Washington, DC, USA |
| Date | 2019 |
| Date | 2019 |
Rights Holder
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All permission requests should be directed accordingly and not to the Sigma Repository.
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.
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