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

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics - 1 Oct 2020

Kadosh Bernard S, Katz Stuart D, Blecker Saul

Abstract excerpt

Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of cases is challenging because of the heterogeneous nature of the disease, which encompasses various phenotypes that may respond differently to treatment. The increasing...

Read the complete abstract on PubMed

Topics

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

Share to Electronic Health Records
Share to Global Health
Share to Heart Failure

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Morbidity

Share to Phenotype

Share to Registries

Source

Identifiers

PMID: 32888634
DOI: 10.1016/j.hfc.2020.05.001

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of Patients with Heart Failure in Large Datasets.PMID 32888634Read ↗

Identifying Heart Failure from Electronic Health Records: A Systematic Evidence ReviewSemantic proximity Read ↗

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.Semantic proximity Read ↗

Identifying subtypes of heart failure with machine learning: external, prognostic and genetic validation in three electronic health record sources with 320,863 individualsSemantic proximity Read ↗

Artificial Intelligence Enabled Phenogrouping of Heart Failure with Preserved Ejection Fraction Depicts Early and End-Stage TrajectoriesSemantic proximity Read ↗

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity Read ↗

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.Semantic proximity Read ↗

Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning.Semantic proximity Read ↗

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.Semantic proximity Read ↗

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and GenomicsSemantic proximity Read ↗

Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions.Semantic proximity Read ↗

Diagnostic signature for Heart Failure with Preserved Ejection Fraction (HFpEF): A Machine Learning Approach Using Multi-Modality Electronic Health Record DataSemantic proximity Read ↗

Phenotyping of Patients With Clinical Features of Advanced Heart Failure: A Latent Class Analysis of a Multicenter Registry of Patients Hospitalized With Heart Failure.Semantic proximity Read ↗

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