Predicting publication inclusion for diagnostic accuracy test reviews using random forests and topic modelling

Finding all relevant publications to perform a systematic review can be a time consuming task, especially in the field of diagnostic test accuracy. Therefore, the CLEF eHealth lab 'technologically assisted reviews in empirical medicine' was established to create a basis of comparison between various methods. In this paper we describe a method submitted to the lab. This method consists of a topic model used to extract features and a random forest to classify the relevant papers. Classifier performance shows and average decrease of 33.3% in workload (i.e., documents to read) when aiming for a 95% recall and 24.9% for 100% recall. However, there is a large variety in workload reduction (79.3% to 0.9%) between the diagnostic test accuracy reviews.