Paper Info
Title
Knowledge discovery on incompatibility of medical concepts
Abstract
This work proposes a method for automatically discovering incompatible medical concepts in text corpora. The approach is distantly supervised based on a seed set of incompatible concept pairs like symptoms or conditions that rule each other out. Two concepts are considered incompatible if their definitions match a template, and contain an antonym pair derived from WordNet, VerbOcean, or a hand-crafted lexicon. Our method creates templates from dependency parse trees of definitional texts, using seed pairs. The templates are applied to a text corpus, and the resulting candidate pairs are categorized and ranked by statistical measures. Since experiments show that the results face semantic ambiguity problems, we further cluster the results into different categories. We applied this approach to the concepts in Unified Medical Language System, Human Phenotype Ontology, and Mammalian Phenotype Ontology. Out of 77,496 definitions, 1,958 concept pairs were detected as incompatible with an average precision of 0.80.
Year
DOI
Venue
2013
10.1007/978-3-642-37247-6_10
CICLing
Keywords
Field
DocType
seed pair,mammalian phenotype ontology,concept pair,text corpus,knowledge discovery,unified medical language system,definitional text,human phenotype ontology,incompatible concept pair,incompatible medical concept,antonym pair
Ontology,Information retrieval,Computer science,Text corpus,Lexicon,Natural language processing,Knowledge extraction,Artificial intelligence,Parsing,WordNet,Unified Medical Language System,Human Phenotype Ontology
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Adam Grycner191.12
Patrick Ernst2706.51
Amy Siu382.83
Gerhard Weikum4127102146.01