Article

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network

2019-06-24

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Objective</h4> Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed ten phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Scienc...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e0536adb-203a-5c7f-914d-64a4b43113a0
DOI: 10.1101/673418

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) NetworkDOI 10.1101/673418Read

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical ReviewSemantic proximity Read

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

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

PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation.Semantic proximity Read

Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.Semantic proximity Read

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.Semantic proximity Read

Cross Biobank Comparison of Phenomic Profiles.Semantic proximity Read

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

CohortDiagnostics: phenotype evaluation across a network of observational data sources using population-level characterizationSemantic proximity Read

Machine learning approaches for electronic health records phenotyping: a methodical review.Semantic proximity Read

UK phenomics platform for developing and validating EHR phenotypes: CALIBERSemantic proximity Read

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.Semantic proximity Read