Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival

Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival

Abstract Background Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship...

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Bibliographic Details
Main Authors: Leah B. Kosyakovsky, Emily Somerset, Angela J. Rogers, Michael Sklar, Jared R. Mayers, Augustin Toma, Yishay Szekely, Sabri Soussi, Bo Wang, Chun-Po S. Fan, Rebecca M. Baron, Patrick R. Lawler
Format: Article
Language:English
Published: SpringerOpen 2022-06-01
Series:Intensive Care Medicine Experimental
Subjects:
Artificial intelligence
Machine learning
Metabolism
Metabolomics
Sepsis
Online Access:https://doi.org/10.1186/s40635-022-00445-8

Internet

https://doi.org/10.1186/s40635-022-00445-8

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