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 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.

Automated summary

This study demonstrates the potential of using Machine Learning techniques to generate meaningful alarms during general anesthesia without constraints on the type of procedure. Two approaches, Complication Detection and Anomaly Detection, were tested. The former achieved the best performance using a simple feed-forward Neural Network, while the latter utilized an Encoder-Decoder Long Short-Term Memory architecture that does not require a large manually-labeled dataset.