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A Context Description Language for Medical Information Systems

  • Kurt EnglmeierEmail author
  • John Atkinson
  • Josiane Mothe
  • Fionn Murtagh
  • Javier Pereira
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)

Abstract

Contextualized delivery of information is one of the many strengths of ubiquitous computing. It makes information actionable and helps us to better understand our situations. In the realm of healthcare, contextual information provides a terse but precise picture of the patient’s health situation. The patient context can have many facets, ranging from nutrition context over health heritage context to the context of symptoms, just to name a few. Setting up the correct health condition context of a patient favors better and faster recognition of the patient’s actual health situation.

Context-awareness in medical monitoring mainly concentrates on gathering numerical facts depicting special aspects of a person’s health condition. In this paper we want to broaden the focus on the textual dimension in context development, by considering semantic annotation in designing context-awareness. We describe an approach for a context description language (CDL) that supports the uniform presentation of textual facts in medical reports and automatic reasoning on these facts. Term clusters in medical reports represent in a unique way symptoms and findings that set up the health context reflected in this particular report. These clusters manifest potential health condition contexts where a patient can be viewed in. A reasoning engine operates on these context presentations and selects those that match best the patient’s health situation. Locating the right context supports the physician in faster getting a first picture of the probable health situation of a new patient to be examined. We present experiments with a CDL applied on reports related to respiratory problems.

Keywords

Context-awareness context design and development semantic annotation domain-specific language information mining natural language interaction medical reports 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Kurt Englmeier
    • 1
    Email author
  • John Atkinson
    • 2
  • Josiane Mothe
    • 3
  • Fionn Murtagh
    • 4
  • Javier Pereira
    • 5
  1. 1.Faculty of Computer ScienceSchmalkalden University of Applied ScienceSchmalkaldenGermany
  2. 2.Department of Computer SciencesUniversidad de ConcepciónConcepciónChile
  3. 3.Institut de Recherche en Informatique de ToulouseUniversité Paul SabatierToulouseFrance
  4. 4.Department of Computer Science, Royal HollowayUniversity of LondonEghamEngland
  5. 5.Facultad de IngenieríaUniversidad Diego PortalesSantiagoChile

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