Please use this identifier to cite or link to this item:
|Title:||Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.|
|Authors:||Joshi, R. |
|Source:||Joshi, R.,Leong, T.Y. (2006). Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium : 404-408. ScholarBank@NUS Repository.|
|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.|
|Source Title:||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 14, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.