Format

Send to

Choose Destination

See 1 citation found by title matching your search:

J Biomed Inform. 2014 Oct;51:280-6. doi: 10.1016/j.jbi.2014.06.007. Epub 2014 Jun 21.

Design patterns for the development of electronic health record-driven phenotype extraction algorithms.

Author information

1
Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Electronic address: luke.rasmussen@northwestern.edu.
2
Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, United States.
3
Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
4
Group Health Research Institute, Seattle, WA, United States.
5
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
6
Marshfield Clinic Research Foundation, Marshfield, WI, United States.
7
Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, United States.
8
Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, United States.

Abstract

BACKGROUND:

Design patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms.

METHODS:

Using phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes.

RESULTS:

A total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement.

CONCLUSIONS:

Repeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy.

KEYWORDS:

Algorithms; Design patterns; Electronic health record; Phenotype; Software design

PMID:
24960203
PMCID:
PMC4194216
DOI:
10.1016/j.jbi.2014.06.007
[Indexed for MEDLINE]
Free PMC Article

Publication type, MeSH terms, Grant support

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center