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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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Deutsch |
| Veröffentlicht: |
September 2022
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| 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
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| Verfasserangaben: | A. Luckscheiter, W. Zink, M. Thiel, T. Viergutz |
MARC
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| 245 | 1 | 0 | |a Evaluation von Entscheidungsbaummodellen des maschinellen Lernens für das akute Leberversagen nach Reanimation |b = Evaluation of decision-tree models of machine learning for the prediction of acute liver failure after resuscitation |c A. Luckscheiter, W. Zink, M. Thiel, T. Viergutz |
| 246 | 3 | 1 | |a Evaluation of decision-tree models of machine learning for the prediction of acute liver failure after resuscitation |
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| 520 | |a 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. | ||
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