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Querying archetype-based Electronic Health Records using Hadoop and Dewey encoding of openEHR models
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Universidade do Estado do Rio de Janeiro, Brazil.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering. (IDA/ADIT)ORCID iD: 0000-0002-9084-0470
2017 (English)In: Informatics for Health: Connected Citizen-Led Wellness and Population Health / [ed] Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J. Scott, Amsterdam, The Netherlands: IOS Press, 2017, p. 406-410Chapter in book (Refereed)
Abstract [en]

Archetype-based Electronic Health Record (EHR) systems using generic reference models from e.g. openEHR, ISO 13606 or CIMI should be easy to update and reconfigure with new types (or versions) of data models or entries, ideally with very limited programming or manual database tweaking. Exploratory research (e.g. epidemiology) leading to ad-hoc querying on a population-wide scale can be a challenge in such environments. This publication describes implementation and test of an archetype-aware Dewey encoding optimization that can be used to produce such systems in environments supporting relational operations, e.g. RDBMs and distributed map-reduce frameworks like Hadoop. Initial testing was done using a nine-node 2.2 GHz quad-core Hadoop cluster querying a dataset consisting of targeted extracts from 4+ million real patient EHRs, query results with sub-minute response time were obtained.

Place, publisher, year, edition, pages
Amsterdam, The Netherlands: IOS Press, 2017. p. 406-410
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 235
Keywords [en]
medical record systems, computerzied; database management systems; Dewey encoding; Archetypes; open EHR; Hadoop; Epidemiology; XML
National Category
Computer Sciences Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-136902DOI: 10.3233/978-1-61499-753-5-406PubMedID: 28423824ISBN: 978-1-61499-752-8 (print)ISBN: 978-1-61499-753-5 (electronic)OAI: oai:DiVA.org:liu-136902DiVA, id: diva2:1091944
Funder
Swedish e‐Science Research CenterAvailable from: 2017-04-28 Created: 2017-04-28 Last updated: 2018-02-09Bibliographically approved

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Sundvall, ErikWei-Kleiner, FangLambrix, Patrick

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