Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data

Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data

Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid “one-size-fits-all” approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatri...

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Bibliographic Details
Main Authors: Yidi Qin, Rebecca I. Caldino Bohn, Aditya Sriram, Kate F. Kernan, Joseph A. Carcillo, Soyeon Kim, Hyun Jung Park
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Pediatrics
Subjects:
biomarker
machine-learning
pediatric sepsis
observational data
clustering
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2023.1035576/full

Internet

https://www.frontiersin.org/articles/10.3389/fped.2023.1035576/full

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