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

Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records.

Health informatics journal - 1 Dec 2018

Vazquez Guillamet Rodrigo, Ursu Oleg, Iwamoto Gary, Moseley Pope L, Oprea Tudor

Abstract excerpt

Chronic obstructive pulmonary disease is a heterogeneous disease. In this retrospective study, we hypothesize that it is possible to identify clinically relevant phenotypes by applying clustering methods to electronic medical records. We included all the patients >40 years with a diagnosis of chronic obstructive pulmonary disease admitted to the University of New Mexico Hospital between 1 January 2011 and 1 May...

Read the complete abstract on PubMed

Topics

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

Share to Aged
Share to Comorbidity
Share to Electronic Health Records

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

Share to Models, Statistical

Share to Phenotype

Share to Pulmonary Disease, Chronic Obstructive

Share to Risk Factors

Source

Identifiers

PMID: 27856785
DOI: 10.1177/1460458216675661

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 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

Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records.PMID 27856785Read ↗

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda.Semantic proximity Read ↗

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health RecordsSemantic proximity Read ↗

Idiopathic pulmonary arterial hypertension phenotypes determined by cluster analysis from the COMPERA registry.Semantic proximity Read ↗

Clusters of comorbidities in idiopathic pulmonary fibrosis.Semantic proximity Read ↗

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.Semantic proximity Read ↗

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.Semantic proximity Read ↗

[Chronic obstructive pulmonary disease phenotypes].Semantic proximity Read ↗

Predicting clinical outcome with phenotypic clusters in COVID-19 pneumonia: an analysis of 12,066 hospitalized patients from the Spanish registry SEMI-COVID-19Semantic proximity Read ↗

Identification of clinical phenotypes using cluster analyses in COPD patients with multiple comorbidities.Semantic proximity Read ↗

Identifying COVID-19 phenotypes using cluster analysis and assessing their clinical outcomesSemantic proximity Read ↗

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

Octogenarian patients with chronic obstructive pulmonary disease: Characteristics and usefulness of prognostic indexes.Semantic proximity Read ↗

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