Automatic text classification to support systematic reviews in medicine

Medical systematic reviews answer particular questions within a very specific domain of expertise by selecting and analysing the current pertinent literature. As part of this process, the phase of screening articles usually requires a long time and significant effort as it involves a group of domain experts evaluating thousands of articles in order to find the relevant instances. Our goal is to support this process through automatic tools. There is a recent trend of applying text classification methods to semi-automate the screening phase by providing decision support to the group of experts, hence helping reduce the required time and effort. In this work, we contribute to this line of work by performing a comprehensive set of text classification experiments on a corpus resulting from an actual systematic review in the area of Internet-Based Randomised Controlled Trials. These experiments involved applying multiple machine learning algorithms combined with several feature selection techniques to different parts of the articles (i.e., titles, abstract, or both). Results are generally positive in terms of overall precision and recall measurements, reaching values of up to 84%. It is also revealing in terms of how using only article titles provides virtually as good results as when adding article abstracts. Based on the positive results, it is clear that text classification can support the screening stage of medical systematic reviews . However, selecting the most appropriate machine learning algorithms, related methods, and text sections of articles is a neglected but important requirement because of its significant impact to the end results.