Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.
Conference Proceeding Article
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.
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
Data Management and Analytics
AMIA Annual Symposium Proceedings 2006
City or Country
Washington DC, USA
Joshi, Rohit and Tze-Yun LEONG.
Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.. (2006). AMIA Annual Symposium Proceedings 2006. 404-408. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3031