PubMed Labs: an experimental system for improving biomedical literature search

Abstract PubMed is a freely accessible system for searching the biomedical literature, with ∼2.5 million users worldwide on an average workday. In order to better meet our users’ needs in an era of information overload, we have recently developed PubMed Labs (www.pubmed.gov/labs), an experimental system for users to test new search features/tools (e.g. Best Match) and provide feedback, which enables us to make more informed decisions about potential changes to improve the search quality and overall usability of PubMed. In addition, PubMed Labs features a mobile-first and responsive layout that offers better support for accessing PubMed from increasingly popular mobiles and small-screen devices. In this paper, we detail PubMed Labs, its purpose, new features and best practices. We also encourage users to share their experience with us; based on which we are continuously improving PubMed Labs with more advanced features and better user experience.

[1]  Mir S. Siadaty,et al.  Bmc Medical Informatics and Decision Making Relemed: Sentence-level Search Engine with Relevance Score for the Medline Database of Biomedical Articles , 2007 .

[2]  Rebecca Nugent,et al.  Medical literature searches: a comparison of PubMed and Google Scholar. , 2012, Health information and libraries journal.

[3]  Zhiyong Lu,et al.  PubMed and beyond: a survey of web tools for searching biomedical literature , 2011, Database J. Biol. Databases Curation.

[4]  Jee-Hyub Kim,et al.  Europe PMC in 2017 , 2017, Nucleic Acids Res..

[5]  Michael Schroeder,et al.  GoPubMed: exploring PubMed with the Gene Ontology , 2005, Nucleic Acids Res..

[6]  Zhiyong Lu,et al.  Best Match: New relevance search for PubMed , 2018, PLoS biology.

[7]  Jimmy J. Lin,et al.  PubMed related articles: a probabilistic topic-based model for content similarity , 2007, BMC Bioinformatics.

[8]  Alfred D. Eaton,et al.  HubMed: a web-based biomedical literature search interface , 2006, Nucleic Acids Res..

[9]  Zhiyong Lu,et al.  Understanding PubMed® user search behavior through log analysis , 2009, Database J. Biol. Databases Curation.

[10]  Zhiyong Lu,et al.  A Field Sensor: computing the composition and intent of PubMed queries , 2018, Database J. Biol. Databases Curation.

[11]  Linda A. Watson,et al.  Information Retrieval: A Health and Biomedical Perspective. , 2005 .

[12]  Stephen E. Robertson,et al.  GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .

[13]  Zhiyong Lu,et al.  Viewpoint Paper: Evaluating Relevance Ranking Strategies for MEDLINE Retrieval , 2009, J. Am. Medical Informatics Assoc..

[14]  Duy Duc An Bui,et al.  Automatically finding relevant citations for clinical guideline development , 2015, J. Biomed. Informatics.

[15]  Zhiyong Lu,et al.  Towards PubMed 2.0 , 2017, eLife.