KNOWLEDGE DISCOVERY AND KNOWLEDGE REUSE IN CLINICAL INFORMATION SYSTEMS

Jon D. Patrick, Leila Safari, Yuzhong Cheng

References

  1. [1] R. T. Snodgrass, The TSQL2 temporal query language(Springer. Vol. 330. 1995).
  2. [2] C. Combi, A. Montanari, & G. Pozzi, The t4sql temporalquery language, in Proceedings of the sixteenth ACM conferenceon Conference on information and knowledge management2007,ACM: Lisbon, Portugal. p. 193-202.
  3. [3] SPARQL Query Language for RDF. [cited 2/4/2012];Available from: http://www.w3.org/TR/rdf-sparql-query/.
  4. [4] M. J. O’Connor, & A. Das, SQWRL: a Query Language forOWL. Proc. of 6th OWL: Experiences and Directions Workshop(OWLED2009), 2009.
  5. [5] J. Tappolet, & A. Bernstein, Applied temporal RDF:Efficient temporal querying of RDF data with SPARQL. TheSemantic Web: Research and Applications, 2009, 308-322.
  6. [6] H. Jung, J. Allen, N. Blaylock, W. de Beaumont, L. Galescu,& M. Swift, Building timelines from narrative clinical records:initial results based-on deep natural language understanding.Proceedings of BioNLP 2011 Workshop: Association forComputational Linguistics,2011, 146-154.
  7. [7] G. Savova, S. Bethard, W. Styler, J. Martin, M. Palmer, J.Masanz, & W. Ward, Towards temporal relation discovery fromthe clinical narrative. AMIA Annual Symposium Proceedings:American Medical Informatics Association,2009, 568.
  8. [8] C. Tao, W. Q. Wei, H. R. Solbrig, G. Savova, & C. G. Chute,CNTRO: A semantic web ontology for temporal relationinferencing in clinical narratives. AMIA Annual SymposiumProceedings: American Medical Informatics Association,2010,787.
  9. [9] L. Zhou, C. Friedman, S. Parsons, & G. Hripcsak, Systemarchitecture for temporal information extraction, representationand reasoning in clinical narrative reports. AMIA AnnualSymposium Proceedings: American Medical InformaticsAssociation,2005, 869.
  10. [10] C. Safran, C. Rury, J. Lightfoot, & D. Porter, ClinQuery: AProgram for Interactive Searching of Clinical Data. Proceedingsof the Annual Symposium on Computer Application in MedicalCare: American Medical Informatics Association,1989, 414.
  11. [11] P. J. Rajkovic, & D. S. Jankovic, Custom made medicaldata reporting tool. Telecommunication in Modern Satellite,Cable, and Broadcasting Services, 2009. TELSIKS'09. 9thInternational Conference on: IEEE,2009, 306-309.
  12. [12] A. Inokuchi, K. Takeda, N. Inaoka, & F. Wakao,MedTAKMI-CDI: interactive knowledge discovery for clinicaldecision intelligence. IBM Systems Journal. 46(1), 2007, 115-133.
  13. [13] D. Klimov, Y. Shahar, & M. Taieb-Maimon, Intelligentvisualization and exploration of time-oriented data of multiplepatients. Artificial intelligence in medicine (Tecklenburg,Germany). 49(1), 2010, 11.
  14. [14] T. D. Wang, K. Wongsuphasawat, C. Plaisant, & B.Shneiderman, Visual information seeking in multiple electronichealth records: design recommendations and a process model.Proceedings of the 1st ACM International Health InformaticsSymposium: ACM,2010, 46-55.
  15. [15] K. Wongsuphasawat, & B. Shneiderman, Findingcomparable temporal categorical records: A similarity measurewith an interactive visualization. Visual Analytics Science andTechnology, 2009. VAST 2009. IEEE Symposium on: IEEE,2009,27-34.
  16. [16] Cancer Text Information Extraction System (caTIES). [cited15/3/2012]; Available from: http://caties.cabig.upmc.edu/.
  17. [17] M. J. O’Connor, M. Bingen, A. Richards, S. W. Tu, & A.Das, Web-Based Exploration of Temporal Data in Biomedicine.7th International Conference on Web Information Systems andTechnologies (WEBIST), Noordwijkerhout, Netherlands, 2011,352-359.
  18. [18] Deductive Reasoning. [cited 15/9/2012]; Available from:http://en.wikipedia.org/wiki/Deductive_reasoning.
  19. [19] J. F. Allen, Maintaining knowledge about temporalintervals. Communications of the ACM. 26(11), 1983, 832-843.

Important Links:

Go Back