Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illnessResearch in context

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illnessResearch in context

Summary: Background: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on...

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Main Authors: Mihir R. Atreya, Shayantan Banerjee, Andrew J. Lautz, Matthew N. Alder, Brian M. Varisco, Hector R. Wong, Jennifer A. Muszynski, Mark W. Hall, L. Nelson Sanchez-Pinto, Rishikesan Kamaleswaran, Natalie Z. Cvijanovich, Julie C. Fitzgerald, Scott L. Weiss, Michael T. Bigham, Parag N. Jain, Adam J. Schwarz, Riad Lutfi, Jeffrey Nowak, Geoffrey L. Allen, Neal J. Thomas, Jocelyn R. Grunwell, Torrey Baines, Michael Quasney, Bereketeab Haileselassie, Chris J. Lindsell
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:EBioMedicine
Subjects:
Critical illness
Sepsis
Multiple organ dysfunction syndrome
Precision medicine
Supervised machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396423005042

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http://www.sciencedirect.com/science/article/pii/S2352396423005042

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