Abstract
Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS.
Highlights
We study an alternative formulation of the intent-aware metric proposed by Umemoto et al [21], in which the authors analyze a metric to estimate the amount of missing information for each query reformulation during a search session
Our research goal is to understand whether a gain based approach can be used to predict the relative importance of each reformulation in terms of recall performance, in the context of Consumer Health Search where users need support for medical information needs
We adapted the definition of gain proposed by Umemoto et al [21] to the context of Consumer Health Search, and we used a standard test collection to evaluate our hypotheses: can we use the gain metric to predict the performance of each reformulation? Is there a better formulation that can produce an order of the importance of each reformulation in terms of recall?
Summary
The study of the query representation in Information Retrieval has driven a lot of interest in recent years [1,2,3,4,5,6,7]. Several works in the past [8,9,10] showed the positive effect on the retrieval results of fusing runs retrieved with human-made multiple formulations of the same information need. Recent studies have shown how query reformulations automatically extracted from query logs can be as effective as those manually created by users [11]. The performance of a system can greatly improve when the “right”
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
