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Integrating Imaging and Clinical Data for Decision Support
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Author(s): William Hsu (University of California at Los Angeles, USA), Alex A.T. Bui (University of California at Los Angeles, USA), Ricky K. Taira (University of California at Los Angeles, USA)and Hooshang Kangarloo (University of California at Los Angeles, USA)
Copyright: 2009
Pages: 16
Source title:
Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Source Author(s)/Editor(s): Themis P. Exarchos (University of Ioannina, Greece ), Athanasios Papadopoulos (University of Ioannina, Greece )and Dimitrios I. Fotiadis (University of Ioannina, Greece )
DOI: 10.4018/978-1-60566-314-2.ch002
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Abstract
Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not sufficiently integrated and organized in a way that effectively supports a clinician’s ability to diagnose and treat a patient. The goal of this chapter is to present a framework for organizing, representing, and manipulating patient data to assist in medical decision-making. We first demonstrate how probabilistic graphical models (specifically, Bayesian belief networks) are capable of representing medical knowledge. We then propose a data model that facilitates temporal and investigative organization by structuring and modeling clinical observations at the patient level. Using information aggregated into the data model, we describe the creation of multi-scale, temporal disease models to represent a disease across a population. Finally, we describe visual tools for interacting with these disease models to facilitate the querying and understanding of results. The chapter concludes with a discussion about open problems and future directions.
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