Creating a Next Generation Phenotype Library : the health data research UK Phenotype Library

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SwanseaUniversityMedical/concept-library: Concept-Library-2.0.13

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Creating a next-generation phenotype library:the health data research UK Phenotype Library

ObjectiveTo enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.Materials and MethodsWe undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly.ResultsWe created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups.DiscussionThis platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes.ConclusionsWe have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.Lay Summary: When people interact with the healthcare system (ie, primary care and/or secondary care), a wealth of information is generated and recorded related to their care. Using this information for research is very challenging as it is very complex. For example, a researcher may want to identify people with depression, identify people at high risk of severe COVID-19 infection, or obtain information on prescriptions of anti-diabetic medications. This is challenging as all this information may be stored in different ways like a set of clinical codes instead of simply the name of the disease or drug, some information may be recorded in patients’ notes. To tackle this problem, researchers define their process (known as phenotyping) to create tools (phenotyping algorithms) that can identify and extract the relevant information from these data. Researchers developing phenotyping algorithms are often used to sharing them as Supplementary material to their research publications or not sharing them at all. This makes it difficult for other researchers to find and re-use previously defined phenotype algorithms. The Health Data Research (HDR) United Kingdom (UK) aimed to develop a library of phenotyping algorithms to promote sharing and reuse. The library also enables researchers to submit their own definitions. As of now, the library hosts 1049 phenotype algorithms and has been widely used by researchers. The library design follows state-of-the-art software design and development principles making it easy to use for researchers to interact with the library directly in their analysis pipeline to re-use existing and share newly developed phenotyping algorithms

Creating a next-generation phenotype library:the health data research UK Phenotype Library

ObjectiveTo enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.Materials and MethodsWe undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly.ResultsWe created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups.DiscussionThis platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes.ConclusionsWe have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.Lay Summary: When people interact with the healthcare system (ie, primary care and/or secondary care), a wealth of information is generated and recorded related to their care. Using this information for research is very challenging as it is very complex. For example, a researcher may want to identify people with depression, identify people at high risk of severe COVID-19 infection, or obtain information on prescriptions of anti-diabetic medications. This is challenging as all this information may be stored in different ways like a set of clinical codes instead of simply the name of the disease or drug, some information may be recorded in patients’ notes. To tackle this problem, researchers define their process (known as phenotyping) to create tools (phenotyping algorithms) that can identify and extract the relevant information from these data. Researchers developing phenotyping algorithms are often used to sharing them as Supplementary material to their research publications or not sharing them at all. This makes it difficult for other researchers to find and re-use previously defined phenotype algorithms. The Health Data Research (HDR) United Kingdom (UK) aimed to develop a library of phenotyping algorithms to promote sharing and reuse. The library also enables researchers to submit their own definitions. As of now, the library hosts 1049 phenotype algorithms and has been widely used by researchers. The library design follows state-of-the-art software design and development principles making it easy to use for researchers to interact with the library directly in their analysis pipeline to re-use existing and share newly developed phenotyping algorithms