Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.

BMC medical informatics and decision making - 27 Jun 2026

Zhang Lin, Li Jiangjing, Shang Xiaolin, Liu Yongfei, Chen Zhiyang, Xiang Hongxia, Zhang Heming

Abstract excerpt

BACKGROUND: The substantial heterogeneity among septic patients with new‑onset atrial fibrillation (NOAF) complicates intensive care unit (ICU) management, and the absence of a parsimonious subphenotype classifier has impeded the implementation of personalized therapeutic strategies. In this study, we aim to identify the subphenotype of septic patients with NOAF, develop a concise classifier model, and reveal the...

Read the complete abstract on PubMed

Topics

Humans
Atrial Fibrillation
Retrospective Studies
Sepsis
Classification Algorithms
Female

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Phenotype

Aged

Male

Middle Aged

Random Forest

Clustering Algorithms

Intensive Care Units

Boosting Machine Learning Algorithms

Source

Open in PubMed Open full text

Identifiers

PMID: 42374411
DOI: 10.1186/s12911-026-03652-5
PMCID: PMC13576258

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 19 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.PMID 42374411Read ↗

Unveiling Clinical Heterogeneity in Statin-Treated Sepsis Patients: A Machine Learning-Based Subphenotyping Study Leveraging the MIMIC-IV DatabaseSemantic proximity Read ↗

Cardiovascular Subphenotypes in Sepsis.Semantic proximity Read ↗

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.Semantic proximity Read ↗

Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.Semantic proximity Read ↗

Development and Validation of Clinical Subphenotypes in Sepsis-Associated Liver Injury Using Multi-Algorithm Consensus Classification: A Retrospective Cohort StudySemantic proximity Read ↗

Clinical phenotype classification to predict risk and optimize the management of patients with atrial fibrillation using the Atrial Fibrillation Better Care (ABC) pathway: a report from the COOL-AF registry.Semantic proximity Read ↗

<p class="MsoNormal" style="margin-bottom: 12.0pt; text-align: left; mso-line-height-alt: 14.0pt; layout-grid-mode: char; mso-layout-grid-align: none;" align="left">Physiological Phenotypes of Sepsis Reveal a Normotensive High-Risk “Cryptic Shock” State: An Unsupervised Machine Learning Analysis of a Multicentre ICU Cohort and External Validation in Mimic-IVSemantic proximity Read ↗

Leading organ-failure phenotype and subsequent new-onset atrial fibrillation in prolonged ICU stays: derivation in MIMIC-IV and external validation in eICU-CRDSemantic proximity Read ↗

A Machine Learning-Derived “Cryptic Shock” Phenotype Provides Incremental Prognostic Value and Supports Bedside Risk Stratification in Elderly Mechanically Ventilated Patients: A Multicenter Study with External ValidationSemantic proximity Read ↗

Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell diseaseSemantic proximity Read ↗

Development and Temporal Validation of the SAKARYA Model for Early Cardiorenal Risk Stratification in Critically Ill Adults with Acute Heart Failure: A Credentialed Database AnalysisSemantic proximity Read ↗

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.