Machine learning in anesthesiology: Detecting adverse events in clinical practice.
The credibility of threshold-based alarms in anesthesia monitors is low and most of the warnings they produce are not informative. This study aims to show that Machine Learning techniques have a potential to generate meaningful alarms during general anesthesia without putting constraints on the type...
| Published in: | Health Informatics Journal Vol. 28; no. 3; pp. 1 - 14 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Sage Publications Inc.
Jul-Sep2022
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| Online Access: | View this record in EBSCOhost |