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

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.

Journal of the American Medical Informatics Association : JAMIA - 1 Nov 2020

Zheng Neil S, Feng QiPing, Kerchberger V Eric, Zhao Juan, Edwards Todd L, Cox Nancy J, Stein C Michael, Roden Dan M, Denny Joshua C, Wei Wei-Qi

Abstract excerpt

OBJECTIVE: Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. MATERIALS AND METHODS: PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that...

Read the complete abstract on PubMed

Topics

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

Share to Dementia
Share to Diabetes Mellitus, Type 2

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electronic Health Records

Share to Hypothyroidism

Share to Information Storage and Retrieval

Share to Knowledge Bases

Share to Natural Language Processing

Share to Terminology as Topic

Source

Open in PubMed Open full text

Identifiers

PMID: 32974638
DOI: 10.1093/jamia/ocaa104
PMCID: PMC7751140

Genes

FTO
TCF7L2

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

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.PMID 32974638Read ↗

Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.Semantic proximity Read ↗

Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Semantic proximity Read ↗

Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes.Semantic proximity Read ↗

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWASSemantic proximity Read ↗

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.Semantic proximity Read ↗

Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian diseaseSemantic proximity Read ↗

Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanksSemantic proximity Read ↗

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.Semantic proximity Read ↗

OARD: Open annotations for rare diseases and their phenotypes based on real-world data.Semantic proximity Read ↗

The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variantsSemantic proximity Read ↗

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping AlgorithmsSemantic proximity Read ↗

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.Semantic proximity Read ↗

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