A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis

Abstract Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. Herein, blood samples from 586 in-house patients with suspected sepsis are used in conjunction with machine learning and cross-validation to...

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Main Authors: Lidija Malic, Peter G. Y. Zhang, Pamela J. Plant, Liviu Clime, Christina Nassif, Dillon Da Fonte, Evan E. Haney, Byeong-Ui Moon, Victor Mun-Sing Sit, Daniel Brassard, Maxence Mounier, Eryn Churcher, James T. Tsoporis, Reza Falsafi, Manjeet Bains, Andrew Baker, Uriel Trahtemberg, Ljuboje Lukic, John C. Marshall, Matthias Geissler, Robert E. W. Hancock, Teodor Veres, Claudia C. dos Santos
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-59227-x
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https://doi.org/10.1038/s41467-025-59227-x

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