“Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes”: algorithmic tautology and feature selection bias

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Publication Details

Journal
Critical Care
Published
2026-08-28
DOI
https://doi.org/10.1186/s13054-026-06277-2
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

“Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes”: algorithmic tautology and feature selection bias

Zejun Yang, YaoTian Zhang
Critical Care
Sepsis Diagnosis and Treatment
article

“Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes”: algorithmic tautology and feature selection bias

Zejun Yang, YaoTian Zhang
article en

Abstract

No abstract available for this paper.
Critical CareVol. 30(1)
Xuzhou Medical College (CN), Second People’s Hospital of Huai’an (CN)
Openalex Percentile: Top 10%
Sepsis Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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“Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes”: algorithmic tautology and feature selection bias — Zejun Yang, YaoTian Zhang · Critical Care (2026) | TGRS Research Map | TGRS