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Identifying relevant studies for systematic reviews and health technology assessments using text mining
103339, £254,294, Medical Research Council Research summary Systematic reviews are a widely used method to bring together the findings from multiple studies in a reliable way, and are often used to inform policy and practice (such as guideline development). However, the large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies both complex and time consuming. Unfortunately the specificity of sensitive electronic searches of bibliographic databases is low. Reviewers often need to look manually through many thousands of irrelevant titles and abstracts in order to identify the much smaller number of relevant ones a process known as 'screening'. Given that an experienced reviewer can take between 30 seconds and several minutes to evaluate a citation, the work involved in screening 10,000 citations is considerable (and the burden of screening is sometimes considerably higher than this). The obvious way to save time in reviews is simply to screen fewer studies. Currently, this is usually accomplished by reducing the number of citations retrieved through electronic searches by developing more specific research strategies, thereby reducing the number of irrelevant citations found. However, limiting the sensitivity of a search may undermine one of the most important principles of a systematic review: that its results are based on an unbiased set of studies. Methodology We therefore propose to develop and evaluate an alternative approach which addresses both of these issues. It is important to have as sensitive a search as is possible, as this is necessary to obtain reliable review findings, but it is also sometimes impossible to screen the number of citations that these sensitive searches will generate. Thus, some form of automation is needed to identify the citations that do, and do not, need to be screened manually. As the data upon which the automation must work are in the form of text, we are looking to the relatively new science of text mining to provide solutions to these problems. There are two ways of using text mining that are particularly promising for assisting with screening in systematic reviews: one aims to prioritise the list of items for manual screening so that the studies at the top of the list are those that are most likely to be relevant ('screening prioritisation'); the second method uses the manually assigned include/exclude categories of studies in order to 'learn' to apply such categorisations automatically ('automatic classification'). As the use of these technologies and the development of validated methods for their use are in their infancy, an important part of the project is outreach: to build interest, capacity and enthusiasm for their use in the future. We would like to encourage you to contact us if you have data that we could use; might like to participate in our study with a 'live' review; or would like to hear more about this work. Our contact details are on the right hand side of this page.
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