Automation of citation screening in pre-clinical systematic reviews

Background The amount of published in vivo studies and the speed researchers are publishing them make it virtually impossible to follow the recent development in the field. Systematic review emerged as a method to summarise and analyse the studies quantitatively and critically but it is often out-of-date due to its lengthy process. Method We invited five machine learning and text-mining groups to build classifiers for identifying publications relevant to neuropathic pain (33814 training publications). We kept 1188 publications for the assessment of the performance of different classifiers. Two groups participated in the next stage: testing their algorithm on datasets labeled for psychosis (11777/2944) and datasets labeled for Vitamin D in multiple sclerosis (train/text: 2038/510). Result The performances (sensitive/specificity) of the most promising classifier built for neuropathic pain are: 95%/84%. The performance for psychosis and Vitamin D in multiple sclerosis datasets are 95%/73% and 100%/45%. Conclusions Machine learning can significantly reduce the irrelevant publications in a systematic review, and save the scientists’ time and money. Classifier algorithms built for one dataset can be reapplied on another dataset in different field. We are building a machine learning service at the back of Systematic Review & Meta-analysis Facility (SyRF).