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

Publication Type

Conference Proceeding Article

Publication Date

11-2006

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.

Keywords

Biological marker, Adult, Article, Artificial intelligence, Artificial neural network, Bayes theorem, Biology, Classification, Coronary artery disease, Decision support system, Female, Genetics, Human, Male, Methodology, Middle aged, Probability, Single nucleotide polymorphism

Discipline

Computer Sciences | Health Information Technology

Publication

AMIA Annual Symposium Proceedings 2006

First Page

404

Last Page

408

City or Country

Washington DC, USA

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