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Erschienen in: Discover Computing 1-2/2016

01.04.2016 | Medical Information Retrieval

How users search and what they search for in the medical domain

Understanding laypeople and experts through query logs

verfasst von: João Palotti, Allan Hanbury, Henning Müller, Charles E. Kahn Jr.

Erschienen in: Discover Computing | Ausgabe 1-2/2016

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Abstract

The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.

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Fußnoten
3
MedlinePlus is a web-based consumer health information system developed by the American National Library of Medicine (NLM): http://​www.​medlineplus.​gov/​.
 
11
A complete list of all semantic types can be found online: http://​metamap.​nlm.​nih.​gov/​SemanticTypesAnd​Groups.​shtml.
 
12
The Random Forest classifier is based on the python machine learning module scikit-learn (http://​scikit-learn.​org/​) Hyper-parameters were optimised using a grid-search approach.
 
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Metadaten
Titel
How users search and what they search for in the medical domain
Understanding laypeople and experts through query logs
verfasst von
João Palotti
Allan Hanbury
Henning Müller
Charles E. Kahn Jr.
Publikationsdatum
01.04.2016
Verlag
Springer Netherlands
Erschienen in
Discover Computing / Ausgabe 1-2/2016
Print ISSN: 2948-2984
Elektronische ISSN: 2948-2992
DOI
https://doi.org/10.1007/s10791-015-9269-8

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