Paper Info
Title
Finding the meaning of medical concept correlations.
Abstract
Correlation identification methods based on concept co-occurrences have been commonly used on medical free texts. However, concepts co-occur for different reasons, and generalizable approaches to determine the meaning of those co-occurrences are needed. In this paper, we propose a new extraction approach that incorporates UMLS and text classification methods to identify the semantics of the relationships between co-occurring concepts in MEDLINE abstracts. The major difficulty of our approach is the lack of annotated sentences for training and testing purposes. We describe how we semi-automatically annotate the sentences with a combination of heuristics and a partially supervised classification method. In our evaluations, we focus on extracting the meaning of only the correlations between drugs or chemicals and disorders, and we limit the meaning to treats and causes. Based on the good performance results, we believe that our approach shows great promise for tackling the difficult relationship-identification problem in medical free text.
Year
Venue
Keywords
2008
AMIA
unified medical language system,natural language processing,artificial intelligence,algorithms,semantics
Field
DocType
ISSN
Information retrieval,Computer science,Heuristics,Artificial intelligence,Natural language processing,MEDLINE,Unified Medical Language System,Semantics
Conference
1942-597X
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
Meliha Yetisgen-Yildiz132834.25
Wanda Pratt21693165.63