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

• 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
• Institution: Singapore Management University

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.