Efficient Context Prediction for Decision Making in Pervasive Health Care Environments: A Case Study

  • Yves VanrompayEmail author
  • Yolande Berbers
Part of the Annals of Information Systems book series (AOIS, volume 13)


Mobile real-time decision support systems (RTDSS) find themselves deployed in a highly dynamic environment. Decision makers must be assisted, ­taking into account the various time-critical requirements. Perhaps even more important is the fact that the quality of the support given by the system depends heavily on the knowledge of the current and future contexts of the system. A DSS should exhibit inherent proactive behaviour and automatically derive the ­decision-making person (DMP)’s needs for specific information from the context that surrounds him/her. We propose to run a DSS on top of a middleware that helps the decision maker to contextualise information. Moreover, we give a set of requirements that the middleware should fulfil to learn, detect, and predict patterns in context to optimise the information flow to the decision maker. The approach is made concrete and validated in a case study in the domain of medical health care. Representative location prediction algorithms are evaluated using an existing dataset.


Decision support systems Context awareness Context prediction Middleware Learning 



The authors would like to thank their partners in the MUSIC-IST project and acknowledge the partial financial support given to this research by the European Union (6th Framework Programme, contract number 35166).


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium

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