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Machine learning in anesthesiology: Detecting adverse events in clinical practice.
- Source :
-
Health informatics journal [Health Informatics J] 2022 Jul-Sep; Vol. 28 (3), pp. 14604582221112855. - Publication Year :
- 2022
-
Abstract
- 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 of procedure. Two distinct approaches were tested - Complication Detection and Anomaly Detection. The former is a generic supervised learning problem and for this a simple feed-forward Neural Network performed best. For the latter, we used an Encoder-Decoder Long Short-Term Memory architecture that does not require a large manually-labeled dataset. We show this approach to be more flexible and in the spirit of Explainable Artificial Intelligence, offering greater potential for future improvement.
Details
- Language :
- English
- ISSN :
- 1741-2811
- Volume :
- 28
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Health informatics journal
- Publication Type :
- Academic Journal
- Accession number :
- 35801667
- Full Text :
- https://doi.org/10.1177/14604582221112855