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User-centered visual analysis using a hybrid reasoning architecture for intensive care units

Authors
Journal
Decision Support Systems
0167-9236
Publisher
Elsevier
Volume
54
Issue
1
Identifiers
DOI: 10.1016/j.dss.2012.06.009
Keywords
  • Intelligent User Interface
  • Visual Computing
  • Connectionist-Symbolic Integration
  • Knowledge Acquisition
  • Intensive Care Units
  • Medical Monitoring
Disciplines
  • Computer Science
  • Medicine

Abstract

Abstract One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care.

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