A Context Description Language for Medical Information Systems
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.
KeywordsContext-awareness context design and development semantic annotation domain-specific language information mining natural language interaction medical reports
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- 1.Mehra, P.: Context-Aware Computing. Beyond Search and Location-Based Services. IEEE Internet Computing 16(2), 12–16 (2012)Google Scholar
- 2.Fröhlich, P., Oulasvirta, A., Baldauf, Nurminen, A.: On the Move, Wirelessly Connected to the World. Communications of the ACM 54(1), 132–138 (2011)Google Scholar
- 6.Lee, J.-H., Kim, J.-T., Lee, H.-K., Paik, E.-H.: Design and implementation of the Geo-Context Engine for semantic social media service. In: Conference for Internet Technology and Secured Transactions (ICITST), pp. 383–387 (2011)Google Scholar
- 7.Klyne, G., Carroll, J.J.: Resource Description Framework (RDF): Concepts and Abstract Syntax (2004), http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/
- 9.McGuiness, D.L., van Harmelen, F.: OWL Web Ontology Language (2004), http://www.w3c.org//TR/owl-features/
- 11.Stahl, T., Voelter, M.: Model-Driven Software Development. Wiley & Sons (2006)Google Scholar
- 13.Iwanska, L.M.: Natural Language Is a Powerful Knowledge Representation System: The UNO Model. In: Iwanska, L.M., Shapiro, S.C. (eds.) Natural Language Processing and Knowledge Representation, pp. 7–64. AAAI Press, Menlo Park (2000)Google Scholar
- 15.Englmeier, K., Koinig, R.: Domain-Specific Deployment and Configuration Language for Composition and Adaptation of Coarse-Grained Services. In: Proceedings of IEEE-SCC 2009 conference, pp. 490–493 (2009)Google Scholar
- 16.WHO: International Classification of Diseases (ICD) (2013), http://www.who.int/classifications/icd/en/ (retrieved on February 20, 2013)