Discovering opinion leaders for medical topics using news articles.

ABSTRACT:

Background

Rapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.

Methods

We employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject exper

SUBMITTER: Jonnalagadda S 

PROVIDER: S-EPMC3338075 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature