Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study

Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study

Abstract Background Sepsis is a major global health problem. However, it lacks a true reference standard for case identification, complicating epidemiologic surveillance. Consensus definitions have changed multiple times, clinicians struggle to identify sepsis at the bedside, and differing identific...

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Main Authors: Na Li, Kiarash Riazi, Jie Pan, Kednapa Thavorn, Jennifer Ziegler, Bram Rochwerg, Hude Quan, Hallie C. Prescott, Peter M. Dodek, Bing Li, Alain Gervais, Allan Garland
Format: Article
Language:English
Published: SpringerOpen 2025-03-01
Series:Intensive Care Medicine Experimental
Subjects:
Sepsis identification
Electronic health records
Unsupervised learning
Clustering
Adult sepsis event
Online Access:https://doi.org/10.1186/s40635-025-00744-w
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https://doi.org/10.1186/s40635-025-00744-w

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