Med-Tree: A user knowledge graph framework for medical applications

The field of context and intelligence, as a topic of pervasive computing, has been gaining considerable momentum. Typically, context-aware intelligence is applied to understand the situation of the users and their behavior with the objective of providing adaptive services that are closely associated with that context. In this work, we have taken an orthogonal approach wherein we attempt to aggregate knowledge and cognition, of the user, on a given topic to build models out of them. The model thus created is analyzed to derive inferences about the user, where the analysis is performed on a graph model comprising topics based information obtained by mining domain specific personal data sources and from certain facts on which the user has expressed fair level of belief. We have explored the possibility of deriving beneficial information by provisioning an appropriate representation of knowledge as belief-graph with specific orientation in healthcare and call this model as Med-Tree. Subject to privacy conditions, we open up the belief-graph model to establish objective based social connections that gets contextually bound. As a next step, such contextually bound ad-hoc networks are subjected to advanced querying process resulting in useful information extraction and inferences. Leveraging on user´s knowledge or the belief-graph, the proposed Med-Tree could help derive benefits towards better personal healthcare and disease management.