Paper Info
Title
SemInf: a burst-based semantic influence model for biomedical topic influence.
Abstract
In this study, we model how biomedical topics influence one another, given they are organized in a topic hierarchy, medical subject headings, in which the edges capture a parent-child/subsumption relationship among topics. This information enables studying influence of topics from a semantic perspective, which might be very important in analyzing topic evolution and is missing from the current literature. We first define a burst-based action for topics, which models upward momentum in popularity (or “elevated occurrences” of the topics), and use it to define two types of influence: accumulation influence and propagation influence. We then propose a model of influence between topics, and develop an efficient algorithm (TIPS) to identify influential topics. Experiments show that our model is successful at identifying influential topics and the algorithm is very efficient.
Year
DOI
Venue
2013
10.1109/JBHI.2013.2285875
IEEE J. Biomedical and Health Informatics
Keywords
DocType
Volume
medical information systems,upward momentum,seminf,parent-child-subsumption relationship,propagation influence,influential topics,information retrieval,bursts,vocabulary,accumulation influence,burst-based action,semantic perspective,efficient algorithm,biomedical topic influence,topic evolution,mesh (medical subject headings),medical subject headings,social influence,topic hierarchy,burst-based semantic influence model,topic hierarchies,semantics,mutual information,computational modeling,accuracy,histograms
Conference
18
Issue
ISSN
Citations 
2
2168-2208
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Dan He113312.54
Douglas Stott Parker Jr.29837.44