Evolving the HDRUK Phenotype Library: Phenotype Creation and Editing

ObjectiveThe HDRUK Phenotype Library (phenotypes.healthdatagateway.org) shares definitions used to measure concepts of interest (such as diagnoses or treatments) within health datasets. It already holds more than 1000 phenotypes, with researchers able to contribute their work via an API. We aimed to create a more user-friendly method of contributing to the Library. ApproachWe designed a phenotype creation workflow enabling users to create and submit new content via web interface. Goals included clarity and ease of use for a broad range of users. ResultsA home page shows researchers’ own content. Researchers can create new phenotypes using a web form, entering metadata such as name, authors, description, and publications. Code lists are defined via one or more rules, including search terms or referring to another phenotype, or by CSV upload. Users can make their phenotypes accessible to a research group or to all authenticated users, as well as publish content on the web. Publication requests are reviewed to ensure content is complete and appropriate. Editing, with full history and version control, is also supported. ConclusionsWe implemented and released the new features. We are currently engaging researchers to get feedback and invite content submission. ImplicationsThe benefit of tools to support research transparency and repeatability is only realized when they are adopted. We hope that a GUI to support phenotype creation will broaden the Library’s user base help it serve as an enabler of higher-quality, more efficient research across the worldwide health data research community.

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Publication Details

Journal
International Journal for Population Data Science
Published
2024-09-10
DOI
https://doi.org/10.23889/ijpds.v9i5.2729
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
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article

Evolving the HDRUK Phenotype Library: Phenotype Creation and Editing

Muhammad A. Elmessary, H. Dele Davies, Ieuan Scanlon, Daniel S. Thayer et al.
International Journal for Population Data Science
Biomedical Text Mining and Ontologies
article

Evolving the HDRUK Phenotype Library: Phenotype Creation and Editing

Muhammad A. Elmessary, H. Dele Davies, Ieuan Scanlon, Daniel S. Thayer, Carla Oliveíra, Spiros Denaxas, Emily Jefferson, Harry Hemingway, Artur Zinnorov, Alex Coldea, J. A. Scanlon
article en

Abstract

ObjectiveThe HDRUK Phenotype Library (phenotypes.healthdatagateway.org) shares definitions used to measure concepts of interest (such as diagnoses or treatments) within health datasets. It already holds more than 1000 phenotypes, with researchers able to contribute their work via an API. We aimed to create a more user-friendly method of contributing to the Library. ApproachWe designed a phenotype creation workflow enabling users to create and submit new content via web interface. Goals included clarity and ease of use for a broad range of users. ResultsA home page shows researchers’ own content. Researchers can create new phenotypes using a web form, entering metadata such as name, authors, description, and publications. Code lists are defined via one or more rules, including search terms or referring to another phenotype, or by CSV upload. Users can make their phenotypes accessible to a research group or to all authenticated users, as well as publish content on the web. Publication requests are reviewed to ensure content is complete and appropriate. Editing, with full history and version control, is also supported. ConclusionsWe implemented and released the new features. We are currently engaging researchers to get feedback and invite content submission. ImplicationsThe benefit of tools to support research transparency and repeatability is only realized when they are adopted. We hope that a GUI to support phenotype creation will broaden the Library’s user base help it serve as an enabler of higher-quality, more efficient research across the worldwide health data research community.

International Journal for Population Data ScienceVol. 9(5)
European Bioinformatics Institute (GB), Swansea University (GB), Health Data Research UK (GB), University College London (GB)
Openalex Percentile: Top 86%
Biomedical Text Mining and Ontologies
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