The accuracy of machine learning to predict cardiac arrest: A systematic review
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- Author(s)
- Details
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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
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.
45th Biennial Convention 2019 Theme: Connect. Collaborate. Catalyze.
| Type | Poster |
| Acquisition | Proxy-submission |
| Review Type | None: Event Material, Invited Presentation |
| Format | Text-based Document |
| Evidence Level | Systematic Review |
| Research Approach | N/A |
| Keywords | Cardiac Arrest; Machine Learning; Prediction |
| Name | 45th Biennial Convention |
| Host | Sigma Theta Tau International |
| Location | Washington, DC, USA |
| Date | 2019 |
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