  Systematic reviews are essential to summarizing the results of different
clinical and social science studies. The first step in a systematic review task
is to identify all the studies relevant to the review. The task of identifying
relevant studies for a given systematic review is usually performed manually,
and as a result, involves substantial amounts of expensive human resource.
Lately, there have been some attempts to reduce this manual effort using active
learning. In this work, we build upon some such existing techniques, and
validate by experimenting on a larger and comprehensive dataset than has been
attempted until now. Our experiments provide insights on the use of different
feature extraction models for different disciplines. More importantly, we
identify that a naive active learning based screening process is biased in
favour of selecting similar documents. We aimed to improve the performance of
the screening process using a novel active learning algorithm with success.
Additionally, we propose a mechanism to choose the best feature extraction
method for a given review.
