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

      research-article
      , BSc, MDiv, , BSc, MCS, PhD , , BSc, MSc, PhD, , BSc, MSc, , BSc, , BSc, , BSc, MBBS, , BSc, PhD, , PhD, , BSc, MBBS, , PhD, , BSc, , BSc, Dipl.-Math, PhD, , BSc, MSc, PhD, DIC, , MSc, , MBBS, MSc, PhD, , BSc, MSc, MBA, PhD, , BSc, , BSc, , BSc, PhD, , BSc, MSc, PhD, , FFPH, FRCP, FMedSci, , BSc, PhD
      JAMIA Open
      Oxford University Press
      electronic health records, phenotyping, public health informatics, algorithms, application programming interface, medical informatics

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          Abstract

          Objective

          To 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 Methods

          We 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.

          Results

          We 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.

          Discussion

          This 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.

          Conclusions

          We 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.

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          Most cited references32

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.

            Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.
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              ClinicalCodes: An Online Clinical Codes Repository to Improve the Validity and Reproducibility of Research Using Electronic Medical Records

              Lists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.
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                Author and article information

                Contributors
                Journal
                JAMIA Open
                JAMIA Open
                jamiaoa
                JAMIA Open
                Oxford University Press
                2574-2531
                July 2024
                17 June 2024
                17 June 2024
                : 7
                : 2
                : ooae049
                Affiliations
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                Health Informatics Centre, School of Medicine, University of Dundee , Dundee, DD1 9SY, United Kingdom
                School of Natural and Computing Sciences, University of Aberdeen , Aberdeen, AB24 3UE, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                Department of Population Health Sciences, King’s College London , London, SE1 1UL, United Kingdom
                Department of Population Health Sciences, King’s College London , London, SE1 1UL, United Kingdom
                Adolescent Mental Health Data Platform and DATAMIND, Swansea University , Swansea, SA2 8PP, United Kingdom
                Adolescent Mental Health Data Platform and DATAMIND, Swansea University , Swansea, SA2 8PP, United Kingdom
                SAIL Databank, Medical School, Swansea University , Swansea, SA2 8PP, United Kingdom
                Institute of Cancer and Genomic Sciences, University of Birmingham , Birmingham, B15 2TT, United Kingdom
                Institute of Cancer and Genomic Sciences, University of Birmingham , Birmingham, B15 2TT, United Kingdom
                Institute of Health Informatics, University College London , London, NW1 2DA, United Kingdom
                School of Public Health and National Heart and Lung Institute, Imperial College London , London, W12 0BZ, United Kingdom
                Health Data Research United Kingdom , London, NW1 2BE, United Kingdom
                Health Data Research United Kingdom , London, NW1 2BE, United Kingdom
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Trust Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Trust Genome Campus , Hinxton, Cambridge, CB10 1SD, United Kingdom
                Institute of Health Informatics, University College London , London, NW1 2DA, United Kingdom
                University College London Hospitals National Institute of Health Research Biomedical Research Centre , London, NW1 2BU, United Kingdom
                British Heart Foundation Data Science Center, Health Data Research United Kingdom , London, NW1 2BE, United Kingdom
                Institute of Health Informatics, University College London , London, NW1 2DA, United Kingdom
                University College London Hospitals National Institute of Health Research Biomedical Research Centre , London, NW1 2BU, United Kingdom
                Health Informatics Centre, School of Medicine, University of Dundee , Dundee, DD1 9SY, United Kingdom
                Health Data Research United Kingdom , London, NW1 2BE, United Kingdom
                Author notes
                Corresponding Author: Shahzad Mumtaz, BSc, MCS, PhD, School of Natural and Computing Sciences, University of Aberdeen, Room 244a, Meston Building, Meston Walk, AB24 3UE, Aberdeen, United Kingdom ( shahzad.mumtaz@ 123456abdn.ac.uk )
                Author information
                https://orcid.org/0000-0003-1847-4362
                https://orcid.org/0000-0003-2606-2405
                https://orcid.org/0000-0002-5242-9701
                https://orcid.org/0000-0001-9612-7791
                https://orcid.org/0000-0003-2992-7582
                Article
                ooae049
                10.1093/jamiaopen/ooae049
                11182945
                38895652
                259a5874-928c-4ba8-a40d-501c34a7f584
                © The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 June 2023
                : 12 February 2024
                : 17 May 2024
                : 20 May 2024
                Page count
                Pages: 11
                Funding
                Funded by: Health Data Research UK, DOI 10.13039/501100023699;
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: Engineering and Physical Sciences Research Council, DOI 10.13039/501100000266;
                Funded by: Economic and Social Research Council, DOI 10.13039/501100000269;
                Funded by: Department of Health and Social Care, DOI 10.13039/501100000276;
                Funded by: Chief Scientist Office of the Scottish Government Health and Social Care Directorates;
                Funded by: Health and Social Care Research and Development Division, DOI 10.13039/501100010756;
                Funded by: Public Health Agency, DOI 10.13039/501100001626;
                Funded by: British Heart Foundation, DOI 10.13039/501100000274;
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Categories
                Research and Applications
                AcademicSubjects/SCI01530
                AcademicSubjects/MED00010
                AcademicSubjects/SCI01060

                electronic health records,phenotyping,public health informatics,algorithms,application programming interface,medical informatics

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