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

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

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 rea...

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Bibliographic Details
Main Authors: Joshi, Rohit, Tze-Yun LEONG
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
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
Computer Sciences
Health Information Technology
Online Access:https://ink.library.smu.edu.sg/sis_research/3031
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Institution: Singapore Management University
Language: English
Description
Summary: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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