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 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.
Joshi
Rohit
R
Medical Computing Lab, School of Computing, National University of Singapore, Singapore.
Leong
Tze Yun
TY
D000328:Adult; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015415:Biomarkers; D002965:Classification / Q000379:methods*; D019295:Computational Biology; D003327:Coronary Disease / Q000235:genetics; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D011336:Probability
D015415:Biomarkers