An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit.
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| Title: | An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. |
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| Authors: | Lyu X; Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.; NEXUS Personalized Health Technologies, ETH Zürich, Schlieren, 8952, Switzerland.; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland., Fan B; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland., Hüser M; Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland., Hartout P; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland.; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany., Gumbsch T; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland., Faltys M; Department of Intensive Care, Austin Hospital, Melbourne, Victoria, 3084, Australia.; Department of Intensive Care Medicine, University Hospital, University of Bern, Switzerland., Merz TM; Cardiovascular Intensive Care Unit, Auckland City Hospital, Auckland, 1023, New Zealand., Rätsch G; Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.; Medical Informatics Unit, Zürich University Hospital, 8091, Switzerland.; AI Center at ETH Zürich, Zürich, 8092, Switzerland.; Department of Biology, ETH Zürich, Zürich, 8093, Switzerland., Borgwardt K; Swiss Institute for Bioinformatics, Lausanne, 1015, Switzerland.; Department of Biosystems Science and Engineering, ETH Zürich, Basel, 4056, Switzerland.; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany. |
| Source: | Bioinformatics (Oxford, England) [Bioinformatics] 2024 Jun 28; Vol. 40 (Suppl 1), pp. i247-i256. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1367-4811 |
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| DOI: | 10.1093/bioinformatics/btae212 |
