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Evaluation von Entscheidungsbaummodellen des maschinellen Lernens für das akute Leberversagen nach Reanimation = Evaluation of decision-tree models of machine learning for the prediction of acute liver failure after resuscitation

Background: Patients after cardiac arrest developing acute liver failure (ALF) show higher fatality rates and worse outcomes. As machine learning is able to support physicians in their decision-making with the help of big data in health records, the aim of this study is to evaluate decision-tree mod...

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Bibliographic Details
Main Authors: Luckscheiter, André Hermann (Author) , Zink, Wolfgang (Author) , Thiel, Manfred (Author) , Viergutz, Tim (Author)
Format: Article (Journal)
Language:German
Published: September 2022
In: Anästhesiologie & Intensivmedizin
Year: 2022, Volume: 63, Issue: 9, Pages: 350-361
ISSN:1439-0256
DOI:10.19224/ai2022.350
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.19224/ai2022.350
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Author Notes:A. Luckscheiter, W. Zink, M. Thiel, T. Viergutz
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Summary:Background: Patients after cardiac arrest developing acute liver failure (ALF) show higher fatality rates and worse outcomes. As machine learning is able to support physicians in their decision-making with the help of big data in health records, the aim of this study is to evaluate decision-tree models for the prediction of ALF after resuscitation.
Item Description:Gesehen am 31.08.2023
Physical Description:Online Resource
ISSN:1439-0256
DOI:10.19224/ai2022.350

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