Automated systems to identify relevant documents in product risk management.

ABSTRACT:

Background

Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from

SUBMITTER: Wee XT 

PROVIDER: S-EPMC3315431 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature