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

Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data.

Prehospital emergency care - 1 Jan 2022

Stemerman Rachel, Bunning Thomas, Grover Joseph, Kitzmiller Rebecca, Patel Mehul D

Abstract excerpt

Objective: Emergency medical services (EMS) provide critical interventions for patients with acute illness and injury and are important in implementing prehospital emergency care research. Retrospective, manual patient record review, the current reference-standard for identifying patient cohorts, requires significant time and financial investment. We developed automated classification models to identify eligible...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Electronic Health Records
Share to Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Natural Language Processing

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 33315497
DOI: 10.1080/10903127.2020.1859658
PMCID: PMC11295293

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

Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data.PMID 33315497Read ↗

A review of approaches to identifying patient phenotype cohorts using electronic health records.Semantic proximity Read ↗

Combining structured and unstructured data in eMRs to create clinically-defined eMR-derived cohortsSemantic proximity Read ↗

Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study.Semantic proximity Read ↗

Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients.Semantic proximity Read ↗

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic proximity Read ↗

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic 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 ↗

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.Semantic proximity Read ↗

Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.Semantic proximity Read ↗

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.Semantic proximity Read ↗

Natural Language Processing to Build a Multicenter Computable Phenotype Library for Adults with Congenital Heart DiseaseSemantic proximity Read ↗

Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data.Semantic proximity Read ↗

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