Machine learning in anesthesiology: Detecting adverse events in clinical practice

• Main Authors: Ballast, A. (Author), Cnossen, F. (Author), Maciąg, T.T (Author), Struys, M.M (Author), van Amsterdam, K. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2022
• Subjects: anesthesiology; decision support system; machine learning; monitoring
• Online Access: View Fulltext in Publisher
• ISBN: 17412811 (ISSN)
• DOI: 10.1177/14604582221112855

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.