Abstract
This chapter discusses that contextual reasoning enables a wide range of novel intelligent e-health systems. The ability to represent a user’s context—health situation, spatio-temporal circumstances, as well as medical proficiency and other parameters—allows a reasoning system to provide information from medical or pharmaceutical expert knowledge that is suitable for the situation. The claims are illustrated with the example of a mobile electronic patient leaflet application called Pharma+, which was developed to provide pharmaceutical product information to illiterate patients visiting a pharmacy in Rwanda. The authors outline a minimal framework for contextual reasoning in the health domain. This context logic framework is formalized as a labeled deductive system. It allows one to represent the three forms of context dependency (partial representation, perspective, and approximation), but is limited in the type of relations that can be represented: context logic is able to handle partial order relations over several domains, and can be implemented in an efficient manner using directed acyclic graphs (DAGs). The authors investigated main relations required for formalizing patient leaflets and whether these can be realized within this simple framework.
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A linear order is a partial order that fulfills the additional constraint of linearity, that is: any two elements can be ordered with respect to each other.
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To improve readability, we omit brackets, in particular outer brackets, if no ambiguity can arise.
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The future of a time point t can be understood, for instance, as the potentially branching time in the interval \([t, \infty]\) and the past of a time point can be denoted by \([-\infty, t]\).
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Note that we cannot infer from the above that the terms have a similar meaning, we only know from the above how they are related with respect to complexity of terminology.
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Acknowledgements
The research described in this article was partially funded by Ricoh Innovations Corporation. The Pharma+ application was developed by Andrew Kinai, Pie Masomo, Alain Shema, and Bonaventure Twagirimana.
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Schmidtke, H., Yu, H., Masomo, P., Kinai, A., Shema, A. (2014). Contextual Reasoning in an Intelligent Electronic Patient Leaflet System. In: Brézillon, P., Gonzalez, A. (eds) Context in Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1887-4_34
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