Buchumschlag

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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Bibliographische Detailangaben
Hauptverfasser: Luckscheiter, André Hermann (VerfasserIn) , Zink, Wolfgang (VerfasserIn) , Thiel, Manfred (VerfasserIn) , Viergutz, Tim (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Deutsch
Veröffentlicht: September 2022
In: Anästhesiologie & Intensivmedizin
Year: 2022, Jahrgang: 63, Heft: 9, Pages: 350-361
ISSN:1439-0256
DOI:10.19224/ai2022.350
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.19224/ai2022.350
Volltext
Verfasserangaben:A. Luckscheiter, W. Zink, M. Thiel, T. Viergutz
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 31.08.2023
Beschreibung:Online Resource
ISSN:1439-0256
DOI:10.19224/ai2022.350

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