Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.

Full Abstract

Representations and inferences that capture a formal notion of "context" are needed to effectively support various analytic and learning tasks in many biomedical applications. In this paper, we formulate patient-specific inference and situation-dependent classification as context-aware reasoning tasks that can be effectively supported in probabilistic graphical networks. We introduce a new probabilistic graphical framework of Context Sensitive Networks (CSNs) to efficiently represent and reason with context-sensitive knowledge. We illustrate how different types of inference in these networks can be handled in a context-dependent manner. We also demonstrate some promising evaluation results based on a set of real-life risk prediction and model classification problems in coronary heart disease.

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Author information

Author/s: Joshi, Rohit (R); Leong, Tze Yun (TY);

Affiliation: Medical Computing Lab, School of Computing, National University of Singapore, Singapore.

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium (AMIA Annu Symp Proc), published in United States. (Language: eng)

Reference: 2006-; vol (issue ) : pp 404-8

Dates: Created 2007/01/22; Completed 2007/09/28; Revised 2008/11/20;

PMID: 17238372, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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