In:
Health Informatics Journal, SAGE Publications, Vol. 28, No. 3 ( 2022-07), p. 146045822211128-
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
1460-4582
,
1741-2811
DOI:
10.1177/14604582221112855
Sprache:
Englisch
Verlag:
SAGE Publications
Publikationsdatum:
2022
ZDB Id:
2070802-6
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