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

Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning.

Resuscitation - 1 Jun 2026

Singh Manveer, Cariou Alain, Varenne Olivier, Picard Fabien, Pham Vincent

Abstract excerpt

BACKGROUND: Patients with myocardial infarction (MI) complicated by out-of-hospital cardiac arrest (OHCA) represent a heterogeneous population with variable outcomes. Data-driven approaches may help uncover clinically meaningful subgroups to improve risk stratification and guide management. METHODS: We applied an unsupervised machine learning analysis using k-means clustering to a prospective cohort of 478...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged
Share to Cluster Analysis
Share to Clustering Algorithms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Myocardial Infarction

Share to Phenotype

Share to Risk Assessment

Share to Risk Factors

Share to Unsupervised Machine Learning

Source

Identifiers

PMID: 41794115
DOI: 10.1016/j.resuscitation.2026.111037

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning.PMID 41794115Read ↗

Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.Semantic proximity Read ↗

Phenotypic clustering of patients hospitalized in intensive cardiac care units: Insights from the ADDICT-ICCU study.Semantic proximity Read ↗

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.Semantic proximity Read ↗

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.Semantic proximity Read ↗

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

Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic DataSemantic proximity Read ↗

Unsupervised learning of early post-arrest brain injury phenotypes.Semantic proximity Read ↗

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

Identifying clinical phenotype clusters in patients with coronary artery disease.Semantic proximity Read ↗

Subgrouping multimorbid patients with ischemic heart disease by means of unsupervised clustering: A cohort study of 72,249 patients defined comprehensively by diagnoses prior to presentationSemantic proximity Read ↗

Latent class analysis for identification of sub-phenotypes predicting prognosis in hospitalized out-of-hospital cardiac arrestSemantic proximity Read ↗

Out of Hospital Cardiac Arrest - New Insights and a Call for a Worldwide Registry and GuidelinesSemantic proximity Read ↗

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