Healthcare Information Fusion Using Context-Aware Agents

Tapia, Dante I.; Fraile, Juan Antonio García; Reboredo, Ana de Luis; Bajo, Javier

doi:10.1007/978-3-642-13769-3_12

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…Various types of data coming from different sources can be treated as context and the behavior of the system is modified in accordance with the context. The focus of this section is on the context fusion for context-aware systems where context fusion is a process of integrating context about an entity into a consistent and useful representation [2,7,8,30,33]. Different systems use different techniques and algorithms for context fusion.…”

Section: Context Fusionmentioning

confidence: 99%

“…They either use mathematical relationships to identify relationships and merge data [32] or use knowledge from past experience [8,30]. Both types are separately discussed in the next two subsections.…”

Section: Context Fusionmentioning

confidence: 99%

“…A context-aware information fusion for healthcare systems was discussed in [30]. A multi-agent system was proposed that located a user in a given context.…”

Section: Context Fusionmentioning

confidence: 99%

“…Logic based models rely on logical inferencing on fusion model [8,30,32]. Relationship between data is described with the help of mathematical model.…”

Section: Context Fusionmentioning

confidence: 99%

“…Context fusion technique presented in [30] used context-aware agents in healthcare domain. A multi-agent architecture used to collect patient information and then fused it for patient monitoring purpose and intelligent analysis of patient context for a time period.…”

Section: Applications and Challengesmentioning

confidence: 99%

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Context Representation and Fusion: Advancements and Opportunities

Khattak

Akbar

Aazam

et al. 2014

Sensors

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The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

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Section: Context Fusionmentioning

confidence: 99%

Section: Context Fusionmentioning

confidence: 99%

“…A context-aware information fusion for healthcare systems was discussed in [30]. A multi-agent system was proposed that located a user in a given context.…”

Section: Context Fusionmentioning

confidence: 99%

“…Logic based models rely on logical inferencing on fusion model [8,30,32]. Relationship between data is described with the help of mathematical model.…”

Section: Context Fusionmentioning

confidence: 99%

Section: Applications and Challengesmentioning

confidence: 99%

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