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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191301721&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191301721 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Jul-Sep2022 vid: 28 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 191301721 10.1177/14604582221112855 191301721 ppf: 1 ppct: 13 formats: tig: atl: Machine learning in anesthesiology: Detecting adverse events in clinical practice. aug: au: Maciąg, Tomasz T van Amsterdam, Kai Ballast, Albertus Cnossen, Fokie Struys, Michel MRF affil: Department of Artificial Intelligence, University of Groningen, Groningen, The Netherlands and Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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