Interactive exploration of healthcare queries

Bampoulidis, Alexandros; Lupu, Mihai; Palotti, João; Metallidis, Sokratis; Brassey, Jon; Hanbury, Allan

doi:10.1109/cbmi.2016.7500275

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…It is, however, known that there are important differences between the two ( 18–21 ). In the biomedical domain there are a few studies aimed at understanding how health information is being searched for and the information needs of domain users such as clinicians, medical researchers or patients ( 18 , 19 , 22–25 ). The two most comprehensive biomedical query log analyses are the study of 1 day of PubMed queries ( 19 ) and the study of 1 month of PubMed queries ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

A Field Sensor: computing the composition and intent of PubMed queries

et al. 2018

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PubMed® is a search engine providing access to a collection of over 27 million biomedical bibliographic records as of 2017. PubMed processes millions of queries a day, and understanding these queries is one of the main building blocks for successful information retrieval. In this work, we present Field Sensor, a domain-specific tool for understanding the composition and predicting the user intent of PubMed queries. Given a query, the Field Sensor infers a field for each token or sequence of tokens in a query in multi-step process that includes syntactic chunking, rule-based tagging and probabilistic field prediction. In this work, the fields of interest are those associated with (meta-)data elements of each PubMed record such as article title, abstract, author name(s), journal title, volume, issue, page and date. We evaluate the accuracy of our algorithm on a human-annotated corpus of 10 000 PubMed queries, as well as a new machine-annotated set of 103 000 PubMed queries. The Field Sensor achieves an accuracy of 93 and 91% on the two corresponding corpora and finds that nearly half of all searches are navigational (e.g. author searches, article title searches etc.) and half are informational (e.g. topical searches). The Field Sensor has been integrated into PubMed since June 2017 to detect informational queries for which results sorted by relevance can be suggested as an alternative to those sorted by the default date sort. In addition, the composition of PubMed queries as computed by the Field Sensor proves to be essential for understanding how users query PubMed.

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