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...
| Publicado en: | Health Informatics Journal Vol. 28; no. 3; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Jul-Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
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