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Article

Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.

BMC emergency medicine - 2 Apr 2026

Jeong Daun, Park Jong Rul, Maeng Seung Jin, Choi Jung Won, Lee Gun Tak, Hwang Sung Yeon, Kim Chulhong, Park Jong Eun, Shin Tae Gun

Abstract excerpt

BACKGROUND: Identifying high-risk patients in emergency departments (EDs) is crucial due to high mortality rate associated with sepsis and for the timely management. This study aimed to conduct unsupervised consensus clustering of patients with suspected sepsis from a heterogeneous ED population. METHODS: This descriptive, retrospective cohort study was conducted between November 2014 and November 2019 on...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 41928117
DOI: 10.1186/s12873-026-01566-z
PMCID: PMC13169702

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