An empirical study on KDIGO-defined acute kidney injury prediction in the 
intensive care unit

Lyu, Xinrui; Fan, Bowen; Hüser, Matthias; Hartout, Philip; Gumbsch, Thomas; Faltys, Martin; Merz, Tobias M.; Rätsch, Gunnar; Borgwardt, Karsten

doi:10.1093/bioinformatics/btae212

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An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit

Lyu, X., Fan, B., Hüser, M., Hartout, P., Gumbsch, T., Faltys, M., et al. (2024). An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinformatics, 40(Suppl_1), i247-i256. doi:10.1093/bioinformatics/btae212.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-8A24-D Version Permalink: https://hdl.handle.net/21.11116/0000-000F-8D67-F

Genre: Journal Article

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Creators:
Lyu, Xinrui¹, Author
Fan, Bowen¹, Author
Hüser, Matthias¹, Author
Hartout, Philip^{2, 3}, Author
Gumbsch, Thomas¹, Author
Faltys, Martin¹, Author
Merz, Tobias M.¹, Author
Rätsch, Gunnar¹, Author
Borgwardt, Karsten², Author

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1External Organizations, ou_persistent22
2Borgwardt, Karsten / Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Max Planck Society, ou_3502542
3IMPRS-ML: Martinsried, Max Planck Institute of Biochemistry, Max Planck Society, Am Klopferspitz 18, 82152 Martinsried, DE, ou_3531125

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Dates: Published Online: 2024-07

Publication Status: Published online

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Identifiers: DOI: 10.1093/bioinformatics/btae212

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Title: Bioinformatics

Source Genre: Journal

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Pages: - Volume / Issue: 40 (Suppl_1) Sequence Number: - Start / End Page: i247 - i256 Identifier: -