No Cover Image

Journal article 570 views 138 downloads

Desiderata for the development of next-generation electronic health record phenotype libraries

Martin Chapman, Shahzad Mumtaz, Luke V Rasmussen, Andreas Karwath, Georgios V Gkoutos, Chuang Gao, Daniel Thayer Orcid Logo, Jennifer A Pacheco, Helen Parkinson, Rachel L Richesson, Emily Jefferson, Spiros Denaxas, Vasa Curcin

GigaScience, Volume: 10, Issue: 9

Swansea University Author: Daniel Thayer Orcid Logo

  • 58307.pdf

    PDF | Version of Record

    © The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

    Download (1.53MB)

Check full text

DOI (Published version): 10.1093/gigascience/giab059

Abstract

BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the...

Full description

Published in: GigaScience
ISSN: 2047-217X
Published: Oxford University Press (OUP) 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58307
first_indexed 2021-10-13T08:34:30Z
last_indexed 2021-11-10T04:25:36Z
id cronfa58307
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-11-09T12:47:02.5128801</datestamp><bib-version>v2</bib-version><id>58307</id><entry>2021-10-13</entry><title>Desiderata for the development of next-generation electronic health record phenotype libraries</title><swanseaauthors><author><sid>e1a6d9b30965cc61a76371fc8a1bf232</sid><ORCID>0000-0003-1847-4362</ORCID><firstname>Daniel</firstname><surname>Thayer</surname><name>Daniel Thayer</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-10-13</date><deptcode>MEDS</deptcode><abstract>BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.MethodsA group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.</abstract><type>Journal Article</type><journal>GigaScience</journal><volume>10</volume><journalNumber>9</journalNumber><paginationStart/><paginationEnd/><publisher>Oxford University Press (OUP)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2047-217X</issnElectronic><keywords>electronic health records; EHR-based phenotyping; computable phenotype; phenotype library</keywords><publishedDay>11</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-09-11</publishedDate><doi>10.1093/gigascience/giab059</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Health Data Research UK</funders><lastEdited>2021-11-09T12:47:02.5128801</lastEdited><Created>2021-10-13T09:30:41.4084802</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Martin</firstname><surname>Chapman</surname><order>1</order></author><author><firstname>Shahzad</firstname><surname>Mumtaz</surname><order>2</order></author><author><firstname>Luke V</firstname><surname>Rasmussen</surname><order>3</order></author><author><firstname>Andreas</firstname><surname>Karwath</surname><order>4</order></author><author><firstname>Georgios V</firstname><surname>Gkoutos</surname><order>5</order></author><author><firstname>Chuang</firstname><surname>Gao</surname><order>6</order></author><author><firstname>Daniel</firstname><surname>Thayer</surname><orcid>0000-0003-1847-4362</orcid><order>7</order></author><author><firstname>Jennifer A</firstname><surname>Pacheco</surname><order>8</order></author><author><firstname>Helen</firstname><surname>Parkinson</surname><order>9</order></author><author><firstname>Rachel L</firstname><surname>Richesson</surname><order>10</order></author><author><firstname>Emily</firstname><surname>Jefferson</surname><order>11</order></author><author><firstname>Spiros</firstname><surname>Denaxas</surname><order>12</order></author><author><firstname>Vasa</firstname><surname>Curcin</surname><order>13</order></author></authors><documents><document><filename>58307__21161__f82374a41837491ba1d91f951aab3912.pdf</filename><originalFilename>58307.pdf</originalFilename><uploaded>2021-10-13T09:35:07.6475548</uploaded><type>Output</type><contentLength>1599989</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2021-11-09T12:47:02.5128801 v2 58307 2021-10-13 Desiderata for the development of next-generation electronic health record phenotype libraries e1a6d9b30965cc61a76371fc8a1bf232 0000-0003-1847-4362 Daniel Thayer Daniel Thayer true false 2021-10-13 MEDS BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.MethodsA group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains. Journal Article GigaScience 10 9 Oxford University Press (OUP) 2047-217X electronic health records; EHR-based phenotyping; computable phenotype; phenotype library 11 9 2021 2021-09-11 10.1093/gigascience/giab059 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University SU Library paid the OA fee (TA Institutional Deal) Health Data Research UK 2021-11-09T12:47:02.5128801 2021-10-13T09:30:41.4084802 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Martin Chapman 1 Shahzad Mumtaz 2 Luke V Rasmussen 3 Andreas Karwath 4 Georgios V Gkoutos 5 Chuang Gao 6 Daniel Thayer 0000-0003-1847-4362 7 Jennifer A Pacheco 8 Helen Parkinson 9 Rachel L Richesson 10 Emily Jefferson 11 Spiros Denaxas 12 Vasa Curcin 13 58307__21161__f82374a41837491ba1d91f951aab3912.pdf 58307.pdf 2021-10-13T09:35:07.6475548 Output 1599989 application/pdf Version of Record true © The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title Desiderata for the development of next-generation electronic health record phenotype libraries
spellingShingle Desiderata for the development of next-generation electronic health record phenotype libraries
Daniel Thayer
title_short Desiderata for the development of next-generation electronic health record phenotype libraries
title_full Desiderata for the development of next-generation electronic health record phenotype libraries
title_fullStr Desiderata for the development of next-generation electronic health record phenotype libraries
title_full_unstemmed Desiderata for the development of next-generation electronic health record phenotype libraries
title_sort Desiderata for the development of next-generation electronic health record phenotype libraries
author_id_str_mv e1a6d9b30965cc61a76371fc8a1bf232
author_id_fullname_str_mv e1a6d9b30965cc61a76371fc8a1bf232_***_Daniel Thayer
author Daniel Thayer
author2 Martin Chapman
Shahzad Mumtaz
Luke V Rasmussen
Andreas Karwath
Georgios V Gkoutos
Chuang Gao
Daniel Thayer
Jennifer A Pacheco
Helen Parkinson
Rachel L Richesson
Emily Jefferson
Spiros Denaxas
Vasa Curcin
format Journal article
container_title GigaScience
container_volume 10
container_issue 9
publishDate 2021
institution Swansea University
issn 2047-217X
doi_str_mv 10.1093/gigascience/giab059
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.MethodsA group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.
published_date 2021-09-11T16:36:52Z
_version_ 1850686950499942400
score 11.08899

Similar Items