Open AccessExtending SemRep to the public health domain
Author(s) -
Rosemblat Graciela,
Resnick Melissa P.,
Auston Ione,
Shin Dongwook,
Sneiderman Charles,
Fizsman Marcelo,
Rindflesch Thomas C.
Publication year - 2013
Publication title -
journal of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1532-2890
pISSN - 1532-2882
DOI - 10.1002/asi.22899
Subject(s) - public health , domain (mathematical analysis) , health promotion , computer science , field (mathematics) , knowledge management , promotion (chess) , visualization , workforce , world wide web , medicine , artificial intelligence , nursing , political science , mathematical analysis , mathematics , politics , pure mathematics , law
We describe the use of a domain‐independent method to extend a natural language processing ( NLP ) application, SemRep ( R indflesch, F iszman, & L ibbus, 2005), based on the knowledge sources afforded by the U nified M edical L anguage S ystem ( UMLS ®; H umphreys, L indberg, S choolman, & B arnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information‐savvy workforce. Natural language processing and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user‐friendly visualization application as a way to summarize results of P ub M ed® searches in this field of knowledge.