Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

Zuccon, Guido, Koopman, Bevan, & Palotti, Joao (2015) Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information. In 37th European Conference on Information Retrieval (ECIR 2015), 29 March - 2 April 2015, Vienna University of Technology, Gusshausstrasse, Vienna.

View at publisher

Abstract

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

11 since deposited on 19 Mar 2015
5 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 82599
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: information retrieval, health search, consumer health search, medical search
DOI: 10.1007/978-3-319-16354-3_62
ISBN: 9783319163536
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer
Deposited On: 19 Mar 2015 22:27
Last Modified: 10 May 2016 01:14

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page