Semantic Technologies for Data Analysis in Health Care

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9982)


A fruitful application of Semantic Technologies in the field of healthcare data analysis has emerged from the collaboration between Oxford and Kaiser Permanente a US healthcare provider (HMO). US HMOs have to annually deliver measurement results on their quality of care to US authorities. One of these sets of measurements is defined in a specification called HEDIS which is infamous amongst data analysts for its complexity. Traditional solutions with either SAS-programs or SQL-queries lead to involved solutions whose maintenance and validation is difficult and binds considerable amount of resources. In this paper we present the project in which we have applied Semantic Technologies to compute the most difficult part of the HEDIS measures. We show that we arrive at a clean, structured and legible encoding of HEDIS in the rule language of the RDF-triple store RDFox. We use RDFox’s reasoning capabilities and SPARQL queries to compute and extract the results. The results of a whole Kaiser Permanente regional branch could be computed in competitive time by RDFox on readily available commodity hardware. Further development and deployment of the project results are envisaged in Kaiser Permanente.


SPARQL Query Semantic Technology Triple Store Healthcare Informatics Declarative Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The project was funded by the DBOnto Platform Grant, the MaSI\(^3\) Fellowship, and Kaiser Permanente. Thanks are particularly due to Alan Abilla, Andy Amster, Patrick Courneya, Paul Glenn, Peter Hendler, Joseph Jentzsch, Scott Kimberly, and Mike Sutten, without whom this project would not have been possible.


  1. 1.
    Apt, K.R., Blair, H.A., Walker, A.: Towards a theory of declarative knowledge. In: Foundations of Deductive Databases and Logic Programming. pp. 89–148 (1988)Google Scholar
  2. 2.
    Benson, T.: Principles of Health Interoperability HL7 and SNOMED. Springer, New York (2010)CrossRefGoogle Scholar
  3. 3.
    Chaussecourte, P., Glimm, B., Horrocks, I., Motik, B., Pierre, L.: The energy management adviser at EDF. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 49–64. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Krötzsch, M.: OWL 2 profiles: an introduction to lightweight ontology languages. In: Eiter, T., Krennwallner, T. (eds.) Reasoning Web 2012. LNCS, vol. 7487, pp. 112–183. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: a highly-scalable RDF store. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  6. 6.
    Ross, K.A.: Modular stratification and magic sets for datalog programs with negation. In: Proceedings of the ACM Symposium on Principles of Database Systems, pp. 161–171 (1990)Google Scholar
  7. 7.
    Slee, V.N.: The international classification of diseases ninth revision (ICD-9). Ann. Intern. Med. 88(3), 424–426 (1978). CrossRefGoogle Scholar
  8. 8.
    Tao, J., Sirin, E., Bao, J., McGuinness, D.L.: Integrity constraints in OWL. In: Proceedings of the 24th AAAI Conference, AAAI 2010, Atlanta, GA, USA (2010).
  9. 9.
    Vizenor, L., Smith, B.: Speech acts and medical records: the ontological nexus. In: Proceedings of the International Joint Meeting EuroMISE 2004 (EuroMISE, Prague, CZ) (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of OxfordOxfordUK
  2. 2.Kaiser PermanenteOaklandUSA
  3. 3.IHTSDOCopenhagenDenmark

Personalised recommendations