Abstract

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.
Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine
Subtitle of host publicationProceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Pages1327-1328
Number of pages2
ISBN (Electronic)978-1-64368-083-5
ISBN (Print)978-1-64368-082-8
DOIs
Publication statusPublished - 16 Jun 2020
Event30th Medical Informatics Europe Conference - Geneva, Switzerland
Duration: 28 Apr 20201 May 2020
https://mie2020.org/

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
Volume270
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference30th Medical Informatics Europe Conference
Abbreviated titleMIE 2020
CountrySwitzerland
CityGeneva
Period28/04/201/05/20
Internet address

Keywords

  • health data
  • phenotype computation
  • data integration
  • ontology

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