QUT ielab at CLEF eHealth 2017 technology assisted reviews track: Initial experiments with learning to rank
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Abstract
In this paper we describe our participation to the CLEF 2017 Technology Assisted Reviews track (TAR). This track aims to evaluate and advance search technologies aimed at supporting the creation of biomedical systematic reviews. In this context, the track explores the task of screening prioritisation: the ranking of studies to be screened for inclusion in a systematic review. Our solution addresses this challenge by developing ranking strategies based on learning to rank techniques and exploiting features derived by the use of the PICO framework. PICO (Population, Intervention, Control or comparison and Outcome) is a technique used in evidence based practice to frame and answer clinical questions and is used extensively in the compilation of systematic reviews. Our experiments show that the use of the PICO-based feature within learning to rank for provides improvements over the use of baseline features alone.