Understanding PubMed® user search behavior through log analysis

By Rezarta Islamaj Dogan, G. Craig Murray, Aurélie Névéol, Zhiyong Lu. Database J. Biol. Databases Curation, 2009

306 citations recorded by the source corpus.

This article reports on a detailed investigation of PubMed users’ needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users’ needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users’ interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users’ decisions. Analysis of characteristics such as these plays a critical role in identifying users’ information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval. Database URL: http://www.ncbi.nlm.nih.gov/PubMed

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ce52e4530e0d85718cb016186b3e979d3191f95e

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Citations in the corpus

Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs (2017, methodology): “We had access to a month of click logs from PubMed R (cid:13) , a biomedical literature search engine serving about 3 million queries a day, 20 re-sults per page (Dogan et al., 2009).”
Some Common Mistakes In IR Evaluation, And How They Can Be Avoided (2018, background): “, [10] points out that the distribution follows the power law, and web search behavior shows similar patterns1 [9].”
A survey of current trends in computational drug repositioning (2016, background): “The biomedical and pharmaceutical knowledge available in literature [70, 71] or databases contains vast amount of information for drugs and diseases, which can be automatically mined [72–74] and retrieved [75], thanks to the recent advances in text mining research [76].”
tmChem: a high performance approach for chemical named entity recognition and normalization (2015, background): “As such, chemicals/drugs are one of the topics most frequently searched in PubMed [2,3].”
NCBI disease corpus: A resource for disease name recognition and concept normalization (2014, background): “of Health, and as a result, it is frequently searched for in literature [1] and on the Internet [2].”