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Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.

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Title: Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.
Authors: Ang, Song Peng1,2 (AUTHOR), Chia, Jia Ee2,3 (AUTHOR), Lee, Eunseuk2,3 (AUTHOR), Lorenzo-Capps, Maria Jose2,4 (AUTHOR), Laezzo, Madison1,4 (AUTHOR), Iglesias, Jose2,4 (AUTHOR) jiglesias23@gmail.com
Source: Journal of Clinical Medicine. Jul2025, Vol. 14 Issue 13, p4450. 14p.
Database: Academic Search Ultimate
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  Data: Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Clinical+Medicine%22">Journal of Clinical Medicine</searchLink>. Jul2025, Vol. 14 Issue 13, p4450. 14p.
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        Value: 10.3390/jcm14134450
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        Text: English
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      – TitleFull: Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.
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            NameFull: Chia, Jia Ee
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            NameFull: Lorenzo-Capps, Maria Jose
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            – D: 01
              M: 07
              Text: Jul2025
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              Y: 2025
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