Abstract
The Intensive Care Unit (ICU) domain generates large volumes of patient data which can be used in medical research. However, inaccuracies often exist in this data and due to the data’s size and domain complexity, automated approaches are required to associate a level of quality and trust with the data. We describe a computational framework to perform such assessments based on semantic web technologies. Linked data enables integration with other datasets, which can be used along with details of the data’s provenance and medical domain knowledge from appropriate ontologies. We have successfully applied the framework to two types of ICUs: general medical and traumatic brain injury.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bizer, C., Cyganiak, R.: Quality-driven information filtering using the WIQA policy framework. Journal of Web Semantics 7(1), 1–10 (2009)
Corsar, D., Moss, L., Sleeman, D., Sim, M.: Supporting the Development of Medical Ontologies. In: Frontiers in Artificial Intelligence and Applications: Formal Ontologies Meet Industry, pp. 114–125 (2009)
Furber, C., Hepp, M.: SWIQA - A Semantic Web Information Quality Assessment Framework. In: Proc. of ECIS (2011)
Gil, Y., Cheney, J., Groth, P., Hartig, O., Miles, S., Moreau, L., Pinheiro da Silva, P. (eds.): Provenance xg final report. W3C Incubator Group Report (December 2010)
Goldberg, S.I., Niemierko, A., Turchin, A.: Analysis of data errors in clinical research databases. In: AMIA Annu. Symp. Proc., vol. 6, pp. 242–246 (2008)
Hogan, W.R., Wagner, M.M.: Accuracy of Data in Computer-based Patient Records. J. Am. Med. Inform. Assoc. 4(5), 342–355 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moss, L., Corsar, D., Piper, I., Kinsella, J. (2013). Trusting Intensive Care Unit (ICU) Medical Data: A Semantic Web Approach. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-38326-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38325-0
Online ISBN: 978-3-642-38326-7
eBook Packages: Computer ScienceComputer Science (R0)