High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

doi:10.1038/s41596-019-0227-6

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

Community

Back to search

Article

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

Nature protocols - 1 Dec 2019

Zhang Yichi, Cai Tianrun, Yu Sheng, Cho Kelly, Hong Chuan, Sun Jiehuan, Huang Jie, Ho Yuk-Lam, Ananthakrishnan Ashwin N, Xia Zongqi, Shaw Stanley Y, Gainer Vivian, Castro Victor, Link Nicholas, Honerlaw Jacqueline, Huang Sicong, Gagnon David, Karlson Elizabeth W, Plenge Robert M, Szolovits Peter, Savova Guergana, Churchill Susanne, O'Donnell Christopher, Murphy Shawn N, Gaziano J Michael, Kohane Isaac, Cai Tianxi, Liao Katherine P

Abstract excerpt

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Data Analysis

Share to Data Interpretation, Statistical

Share to Electronic Health Records

Share to High-Throughput Screening Assays

Share to Machine Learning

Share to Natural Language Processing

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 31748751
DOI: 10.1038/s41596-019-0227-6
PMCID: PMC7323894

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).PMID 31748751Read ↗

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

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

pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.Semantic proximity Read ↗

MIMIC-IV Phenotype Atlas (MIPA): A Publicly Available Dataset for EHR PhenotypingSemantic proximity Read ↗

Bridging clinical narratives and structured phenotypes with large language models and sentence transformers.Semantic proximity Read ↗

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.Semantic proximity Read ↗

Facilitating phenotyping from clinical texts: the medkit library.Semantic proximity Read ↗

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

Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports.Semantic proximity Read ↗

sureLDA: A Multi-Disease Automated Phenotyping Method for the Electronic Health RecordSemantic proximity Read ↗

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

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

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