Desiderata for the development of next-generation electronic health record phenotype libraries.
EHR-based phenotyping
computable phenotype
electronic health records
phenotype library
Journal
GigaScience
ISSN: 2047-217X
Titre abrégé: Gigascience
Pays: United States
ID NLM: 101596872
Informations de publication
Date de publication:
11 09 2021
11 09 2021
Historique:
received:
24
05
2021
revised:
15
07
2021
accepted:
18
08
2021
entrez:
11
9
2021
pubmed:
12
9
2021
medline:
15
3
2022
Statut:
ppublish
Résumé
High-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. A 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. We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. There 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.
Sections du résumé
BACKGROUND
High-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.
METHODS
A 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.
RESULTS
We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.
CONCLUSIONS
There 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.
Identifiants
pubmed: 34508578
pii: 6368631
doi: 10.1093/gigascience/giab059
pmc: PMC8434766
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Medical Research Council
ID : MR/K006584/1
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_19002
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : U01 HG011169
Pays : United States
Organisme : Medical Research Council
ID : MR/S003991/1
Pays : United Kingdom
Organisme : NIGMS NIH HHS
ID : R01 GM105688
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press GigaScience.
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