Abstract
BackgroundFinding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles—or as we call them documents—by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine.ResultsTo provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents.ConclusionsWe introduce the A2A search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.
Highlights
Finding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine
While EvALL is a generalised platform focused on benchmarking locally deployed systems, we focus solely on biomedical Information Retrieval (IR), with its unique challenges and methods, which include dealing with biomedical vocabularies such as Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH)
Use case 2: adding ‘solid’ to cancer queries In our use case 2, we present an evaluation of a widely adopted approach of reformulating Text Retrieval Conference (TREC) Precision Medicine (2017–2020) [11–13] (PM) queries for clinical trials retrieval by adding terms ‘solid tumor’ with a lower term weight for topics regarding non-blood cancers [32, 33]
Summary
To provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents
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