Systematic literature reviews play a vital role in identifying the best available evidence for
health and social care research, policy, and practice. The resources required to produce …

Screening prioritisation in medical systematic reviews aims to rank the set of documents
retrieved by complex Boolean queries. Prioritising the most important documents ensures …

Thanks to recent advances in IR and NLP, the way users interact with search engines is
evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries …

In this work, we propose a novel framework to devise features that can be used by Query
Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the …

The query performance prediction (QPP) task is to estimate the effectiveness of a search
performed in response to a query with no relevance judgments. Existing QPP methods do …

Query performance prediction (QPP) aims to estimate the retrieval quality of a search system
for a query without human relevance judgments. Previous QPP methods typically return a …

While continuous active learning algorithms have proven effective in finding most of the
relevant documents in a collection, the cost for locating the last few remains high for …

In the medical domain, systematic reviews are a highly trustworthy evidence source used to
inform clinical diagnosis and treatment, and governmental policy making. Systematic …

Systematic reviews form the cornerstone of evidence based medicine, aiming to answer
complex medical questions based on all evidence currently available. Key to the …

The main goal of this book is to provide an understanding of the theory, implementation, and
evaluation of information retrieval (IR) systems in biomedicine and health. There is already a …