Machine learning in anesthesiology:Detecting adverse events in clinical practice

  • Maciag, Tomasz T.
  • van Amsterdam, Kai
  • Ballast, Albertus
  • Cnossen, Fokie
  • Struys, Michel M. R. F.
Open PDF
Publication date
July 2022
Language
English

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...

Extracted data

We use cookies to provide a better user experience.