2 resultados para Internet Information Discovery

em Aston University Research Archive


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Objective To investigate current use of the internet and eHealth amongst adults. Design Focus groups were conducted to explore participants' attitudes to and reasons for health internet use. Main outcome measures The focus group data were analysed and interpreted using thematic analysis. Results Three superordinate themes exploring eHealth behaviours were identified: decline in expert authority, pervasiveness of health information on the internet and empowerment. Results showed participants enjoyed the immediate benefits of eHealth information and felt empowered by increased knowledge, but they would be reluctant to lose face-to-face consultations with their GP. Conclusions Our findings illustrate changes in patient identity and a decline in expert authority with ramifications for the practitioner–patient relationship and subsequent implications for health management more generally.

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In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.